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JMAN is the commercial data partner that specializes in maximizing value creation activities for private equity funds and their portfolio companies. We partner with our clients to address the growing need for investment decisions and value creation initiatives to be backed by reliable, real-time data. When companies partner with JMAN, we combine our data science and data engineering expertise with our deep commercial understanding to deliver tangible, high-value outcomes at pace. Founded in 2010, JMAN has a global footprint with offices in New York, London and Chennai. Our team of more than 350 experts partner with more than 80 private equity funds and over 200 portfolio companies. Nearly 85% of our business is from recurring partnerships with our clients. JMAN has been a portfolio company of Baird Capital since 2023. Position We are a rapidly scaling business focused on delivering applied data science solutions that create measurable commercial value. While we work across a range of AI paradigms, data science sits at the core of what we do, and we are looking for individuals with deep, proven expertise in this area. This is a technical delivery leadership role for a highly experienced Data Scientist who leads with structured problem solving and client empathy. The primary measure of success is whether clients achieve meaningful business outcomes through robust, well-designed data science solutions, not the adoption of the latest AI trends. You will own the full lifecycle of data science engagements: shaping ambiguous problems, designing and delivering statistical and machine learning models, and ensuring they are embedded into operational workflows. You will work across organisations at every stage of data maturity, applying sound judgement to determine what is feasible, practical, and commercially valuable. While the role may involve exposure to Generative AI and autonomous AI, these are not substitutes for strong data science fundamentals. Candidates must demonstrate deep, hands on experience delivering applied machine learning and statistical modelling in real world settings. Internally, you will act as a data science standard setter, helping define best practice, raising the quality bar, and developing others within the team. Scope of Work Our work spans multiple AI paradigms, with a clear emphasis on Data Science as the primary discipline for this role: Data Science (Primary Focus): Statistical modelling, machine learning, predictive analytics, and decision science delivered into production environments and real business processes. Generative AI (Secondary): LLM powered applications such as RAG pipelines and document intelligence, applied where appropriate to support data led solutions. Autonomous AI (Emerging): Agent based systems and AI workflows, used selectively where they add clear value. 75-80% of the role is focused on Data Science led delivery, including problem structuring, model development, evaluation, and operationalisation. The remaining time is spent on client engagement, team leadership, and broader AI exposure where relevant. Core Responsibilities Own end-to-end delivery of data science workstreams, from problem definition through to production deployment and ongoing monitoring. Translate ambiguous business problems into hypothesis driven data science solutions, grounded in statistical rigour and practical feasibility. Lead the design and deployment of machine learning models, ensuring they are robust, explainable, and operationally viable. Provide technical oversight across projects, with a strong focus on model quality, evaluation frameworks, and reproducibility. Work closely with data engineers to ensure models are effectively integrated into production systems, with appropriate pipelines, monitoring, and lifecycle management. Apply sound judgement in selecting modelling approaches, balancing sophistication with interpretability, maintainability, and business value. Act as a trusted advisor to clients, communicating complex data science concepts clearly and influencing decision making at senior levels. Manage delivery timelines, risks, and priorities across engagements. Set the standard for data science excellence, including coding standards, documentation, and best practices. Mentor junior team members, supporting their development as data scientists. The most important requirement for this role is proven, hands on experience in Data Science. Candidates must be able to demonstrate a strong track record of delivering end to end machine learning or statistical solutions in real world environments. Requirements 5+ years' experience in Data Science or applied machine learning, ideally in consulting, high growth, or commercial environments. Strong applied machine learning expertise, including: Model selection and development Feature engineering Evaluation and validation Deployment into production environments Demonstrated experience taking models beyond experimentation into operational, business critical systems. Strong background in statistics and quantitative problem solving. Proficiency in Python and SQL, with experience writing production quality, maintainable code. Experience with modern data science tooling, including: Version control (Git) Cloud platforms (AWS, GCP, or Azure) Experiment tracking and model management Workflow orchestration and containerisation Strong understanding of MLOps principles, including model monitoring, retraining, and lifecycle management. Proven ability to translate business problems into data science solutions that deliver measurable outcomes. Beneficial Exposure to Generative AI (e.g. LLMs, RAG) is beneficial but not a substitute for core data science expertise. Awareness of emerging AI paradigms (e.g. agent based systems) is a plus, but not required. We particularly value depth in applied data science areas such as: Customer analytics (segmentation, churn, LTV) Pricing and optimisation models Experimental design and causal inference Natural Language Processing (within a traditional ML framework as well as LLM based approaches) Other information If you feel that you would be a strong addition to our team, but you do not fully meet all the requirements above, we would like to encourage you to please apply anyway. As we expand, we are looking for individuals across all levels and maybe able to discuss a suitable alternative with you. JMAN is committed to equal employment opportunities. We are a diverse, high performing team and base all our employment decisions on merit, job requirements and business needs. Other information Discretionary bonus - based on personal and company performance 25 days annual leave + bank holidays Pension with a company contribution up to 8% Health Insurance from day 1 Life insurance and long term disability insurance Market leading parental leave policy Salary Sacrifice Nursery Scheme through YellowNest Salary Sacrifice EV Scheme with Octopus Vehicles Additional Health Cash Plan with MediCash Cycle to work scheme Re ferral bonus for bringing in new JMAN hires Extensive training and coaching opportunities Regular company socials and retreats Hybrid working - minimum of 3 days in the office (E3N)

About Relation Relation is a sector defining TechBio company developing transformational medicines, with technology at our core. Our ambition is to understand human biology in unprecedented ways, discovering therapies to treat some of life's most devastating diseases. We leverage single-cell multi-omics from patient tissue, functional assays, and machine learning to drive disease understanding, from cause to cure. We are scaling rapidly and building a team of exceptional individuals to push the boundaries of drug discovery. You will work in highly interdisciplinary teams where biology, computation, and engineering come together to solve complex problems that have not been solved before. Our state-of-the-art wet and dry labs in the heart of London are designed to accelerate this integration and translate insight into impact. We are committed to building diverse and inclusive teams. Relation is an equal opportunities employer and does not discriminate on the basis of gender, sexual orientation, marital or civil partnership status, gender reassignment, race, colour, nationality, ethnic or national origin, religion or belief, disability, or age. By joining Relation, you will help define how medicines are discovered and deliver meaningful impact for patients. The opportunity As Associate Director, Platform Engineering, you will lead the team responsible for the hybrid compute infrastructure, developer platform and scientific compute environments that power Relation's machine learning and drug discovery work. Reporting to the VP of Engineering, you will work closely with your team and peers across Engineering, Data Science and Machine Learning to define and evolve Relation's platform strategy. Our platform spans on-premises infrastructure and public cloud environments, supporting computational scientist notebooks through Kubeflow, model training and serving, internal services and genomic pipelines. You will set the technical direction across these areas, build and develop the team responsible for delivering them, and ensure our platform continues to evolve alongside the needs of our science. Day to day, you will Partner with the VP of Engineering and peers across Engineering, Data Science and Machine Learning to define and evolve Relation's platform strategy. Own the architectural strategy for our Kubernetes-based clusters and broader hybrid infrastructure, setting the technical standards and guardrails within which the team designs, builds and operates. Lead and develop the Platform Engineering team, hiring and retaining exceptional engineers, setting clear expectations and creating the conditions for them to do their best work. Own the scientific compute platform, ensuring it reliably and efficiently meets the evolving needs of our Machine Learning and Data Science teams. Drive the maturity of our CI/CD pipelines, internal services and developer tooling, treating the platform as a product and our internal engineers and computational scientists as its customers. Establish comprehensive observability and robust disaster recovery and failover strategies across on-premises and cloud environments. Own Relation's platform security controls, including IAM, secrets management, network policy and vulnerability management, ensuring the platform meets the data governance standards required of a clinical-stage biotech working with sensitive patient-derived data. Professionally, you will have Significant experience in platform, infrastructure or DevOps engineering, with a strong track record of building and operating complex production environments. Experience leading engineers, either formally or informally, with responsibility for significant workstreams from strategy through to delivery. You have set technical direction others have followed, mentored and developed engineers, and taken accountability for live production systems. Strong hands on operational experience. You have participated in on call rotations for production infrastructure, led responses to significant incidents and driven the subsequent changes required to prevent recurrence. Deep expertise in Kubernetes, alongside experience operating infrastructure across hybrid on premises and cloud environments. Experience building, running or evolving MLOps infrastructure, ideally including notebook environments such as Kubeflow, GPU compute for distributed training and model serving. Familiarity with batch compute for data intensive scientific workloads, with an understanding of the reliability, scalability and reproducibility requirements of scientific data pipelines. A track record of maturing CI/CD pipelines, developer tooling and observability across complex, multi environment platforms. Strong security fundamentals, with hands on experience designing and implementing appropriate security controls. Excellent written and verbal communication skills, with the ability to co own strategy with senior stakeholders, challenge decisions constructively and communicate platform strategy clearly to both technical and non technical audiences. Bonus Experience: Exposure to relevant compliance and governance frameworks, such as ISO 27001, SOC 2 or GDPR within a research or scientific environment. Personally, you Are comfortable working in a matrixed environment, balancing multiple stakeholders and contributing effectively across teams. Take ownership of your work, proactively seek opportunities to contribute, and enable others to do their best work. Technically deep enough to earn the respect of a highly skilled engineering team, while being equally comfortable influencing and contributing at leadership level Communicate openly and directly, give and receive feedback constructively, and handle challenging conversations with respect. Actively seek out diverse perspectives, build strong working relationships, and contribute to shared goals across teams. Embrace challenges with openness and resilience, set high standards for yourself, and strive to deliver meaningful outcomes. Working Style & Culture at Relation At Relation, we operate in a matrixed, interdisciplinary environment, where impact is driven through collaboration across scientific, technical, and operational domains. We collaborate, and you will partner with colleagues across multiple teams and projects, contributing your expertise while aligning to shared company priorities. We work together and win together! The patient is waiting! Recruitment Agencies Please note that Relation does not accept unsolicited resumes from agencies. Resumes should not be forwarded to our job aliases or employees. Relation will not be liable for any fees associated with unsolicited CVs.

Senior AI Risk Technical Advisory Lead Location London Business Area Legal, Compliance, and Risk Ref # Description & Requirements Description The energy of a newsroom, the pace of a trading floor, the buzz of a recent tech breakthrough; we work hard, and we work fast, while keeping up the quality and accuracy we are known for. It is what keeps us inventing and reinventing, all the time. Our culture is wide open, just like our spaces. We bring out the best in each other through collaboration. Through our countless volunteer projects, we also help network with the communities around us. You can do amazing work here. Work you could not do anywhere else. It is up to you to make it happen. Bloomberg's Chief Risk Office plays a central role in ensuring that innovation is pursued responsibly across our global operations. As AI becomes increasingly embedded in Bloomberg's products, platforms, internal workflows, and client-facing capabilities, the Chief Risk Office is building practical advisory capabilities that help technical and product teams identify, assess, mitigate, and monitor AI risks throughout the lifecycle. What's the role? We are seeking a Senior AI Risk Technical Advisory Lead to serve as a senior subject matter expert for AI risk and responsible AI across Bloomberg. This person will work closely with engineering, product, data science, legal, compliance, CISO, privacy, and risk stakeholders to provide practical, technically credible guidance on AI use cases, AI systems and data-related controls, risk assessments of AI systems, risk/control evaluation of third-party AI solutions, and AI regulatory expectations (e.g., EU AI Act) and industry standards. This role is designed for a senior practitioner who can bridge technical AI implementation and enterprise risk oversight. The person may have a matrixed or dual-reporting relationship to a technology or product-facing risk advisory leader to ensure strong connectivity with AI builders and front-line advisory teams, while maintaining alignment with the Chief Risk Office's enterprise AI risk team. We'll trust you to: Technical AI Risk Advisory Provide senior-level technical advisory support for AI and generative AI use cases across Bloomberg, including product, platform, internal tooling, automation, and third-party AI solutions. Evaluate AI risks related to bias, explainability, hallucination, robustness, model drift, data leakage, privacy, security, intellectual property, misuse, transparency, and human oversight. Advise teams on appropriate controls, testing, monitoring, documentation, and risk mitigation strategies based on use case, data sensitivity, model type, deployment context, and regulatory exposure. Review complex or higher-risk AI use cases and provide risk-based recommendations to governance forums and senior stakeholders. Help define practical technical standards for responsible AI development, testing, validation, deployment, monitoring, and retirement. Framework and Control Development Partner with the Head of AI Risk Management to mature Bloomberg's AI risk management framework, including classification, risk tiering, assessment methodology, control expectations, and monitoring standards. Translate external frameworks and regulatory expectations into practical internal control guidance for technical and product teams. Develop reusable advisory materials, playbooks, review criteria, technical checklists, and model documentation expectations. Support the development of risk indicators and monitoring approaches for AI systems, including performance, drift, hallucination, bias, security, user feedback, and issue trends. Cross-Functional Partnership Partner deeply with Technology, Product, Data, Legal, Compliance, CISO, Privacy, Procurement, and business teams to address emerging AI risks. Facilitate technical risk discussions and help resolve questions where product goals, engineering constraints, legal obligations, and risk appetite intersect. Serve as a senior escalation point for complex AI risk questions and emerging responsible AI issues. Support AI risk governance forums, executive updates, regulatory inquiries, and internal reviews. Enablement and Thought Leadership Serve as a trusted internal subject matter expert on responsible AI, technical AI risk, and AI control design. Develop and deliver training, guidance, and awareness materials for technical and non-technical stakeholders. Monitor developments in AI technology, AI regulation, industry standards, and emerging risk practices, and translate them into actionable program enhancements. You'll need to have: 10+ years of experience in AI/ML, data science, machine learning engineering, technology risk, model risk, security risk, data risk, or responsible AI. 4+ years of experience directly focused on AI/ML risk, AI governance, model governance, model validation, responsible AI, or technical advisory for AI systems. Strong technical understanding of AI/ML and generative AI systems, including model development, model evaluation, data pipelines, embeddings, retrieval-augmented generation, prompt engineering, monitoring, drift, robustness, explainability, and safety testing. Demonstrated ability to assess technical AI risks and recommend practical controls in complex enterprise environments. Hands-on familiarity with generative AI platforms and tools, open-source models, vector databases, or related AI infrastructure. Experience partnering with engineering, product, data science, legal, compliance, privacy, security, and risk stakeholders. Strong understanding of privacy, security, data governance, and regulatory issues relevant to AI systems. Excellent communication skills, including the ability to explain technical AI risks to senior stakeholders and translate governance expectations for technical teams. We'd love to see: Experience working directly with AI/ML development teams on production systems. Experience in financial services, market data, media, enterprise technology, cloud, or other data-intensive environments. Familiarity with NIST AI RMF, ISO/IEC 23894, EU AI Act, OECD AI Principles, model risk management, or responsible AI frameworks. Experience with AI red teaming, model evaluation, bias testing, explainability tooling, monitoring tools, MLOps, LLMOps, or AI governance platforms. Experience advising on third-party AI tools, enterprise AI platforms, or AI-enabled vendor solutions. Certifications or advanced training in AI/ML, data science, privacy, information security, risk, or compliance. A pragmatic approach to enabling innovation while managing risk. If indicated, please note that years of experience are a guide; we will consider applications from all candidates who can demonstrate the skills necessary for the role. Discover what makes Bloomberg unique - watch our for an inside look at our culture, values, and the people behind our success. Bloomberg is an equal opportunity employer and we value diversity at our company. We do not discriminate on the basis of age, ancestry, color, gender identity or expression, genetic predisposition or carrier status, marital status, national or ethnic origin, race, religion or belief, sex, sexual orientation, sexual and other reproductive health decisions, parental or caring status, physical or mental disability, pregnancy or parental leave, protected veteran status, status as a victim of domestic violence, or any other classification protected by applicable law. Bloomberg is a disability inclusive employer. Please let us know if you require any reasonable adjustments to be made for the recruitment process. If you would prefer to discuss this confidentially, please email

Databricks Inc. is seeking a Senior Specialist Solutions Architect (ML & AI) to act as a trusted technical ML/AI expert for customers and Field Engineering. You will guide enterprise customers in architecting production-grade ML/AI apps on the Databricks Data Intelligence Platform and mentor colleagues. You will work with solution architects, lead GenAI initiatives, and influence the AI roadmap while maintaining cutting-edge expertise in areas like GenAI, MLOps, and LLMOps.

ReqID: FEQ427R340 Location: London Skills: Data Science, Machine Learning, AI, LLM, GenAI Mission As a Senior Specialist Solutions Architect (ML & AI), you will serve as the trusted technical ML and AI expert for the company customers and the Field Engineering organization. You will partner with Solution Architects to guide enterprise and strategic customers in architecting production-grade ML and AI applications on the the company Data Intelligence Platform. You will also continue to sharpen your technical expertise in cutting-edge areas like GenAI, ML, MLOps, and LLMOps, while mentoring colleagues and establishing yourself as an AI thought leader. Impact you will have Architecting Workloads: Design and implement production-level ML and AI workloads, including end-to-end pipelines, training/inference optimization, MLOps lifecycle management, and integration with cloud-native services. GenAI Leadership: Serve as a practitioner for enterprise GenAI solutions, specializing in RAG architectures, agentic systems (including tool-calling, multi-agent orchestration, and guardrails), AI observability, and natural language querying of structured data. Provide advanced technical support to Solution Architects during the technical sales cycle by building MVPs, leading deep-dive sessions, and aligning AI solutions with complex customer business challenges. Product Influence: Collaborate cross-functionally with product and engineering teams to represent the voice of the customer, define priorities, and influence the platform's AI roadmap. Thought Leadership: Drive community growth and AI platform adoption through the creation of technical tutorials and training materials, as well as by presenting at industry conferences and leading hackathons. What we look for Experience: 10+ years of hands-on industry DS/ML experience, with a focus on either: ML Engineering: Building/maintaining production-grade cloud infrastructure (AWS/Azure/GCP) that supports deployment of ML applications and monitoring ML model performance. Data Science/AI: Applying advanced techniques in LLMs, agentic systems, vector databases, fine-tuning, and deployment tools (e.g., HuggingFace, Langchain). Hands-on experience working with Distributed Spark based systems Experience with data engineering concepts or a good understanding of data engineering concepts Pre-sales or post-sales experience working with external clients across a variety of industry markets. Minimum of 5+ years of customer-facing experience would be preferred Preferred Experience working with Apache Spark to process large-scale distributed datasets Communication: Proven ability to communicate and teach complex technical concepts to both technical and non-technical audiences. Core Traits: Passion for lifelong learning, collaboration, and driving business value through AI. Education: Graduate degree in a quantitative discipline (e.g., Computer Science, Engineering, Statistics, Operations Research, etc) or equivalent practical experience. Can meet expectations for technical training and role-specific outcomes within 3 months of hire Can travel up to 30% when needed About the company the company is the data and AI company. More than 10,000 organizations worldwide - including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 - rely on the the company Data Intelligence Platform to unify and democratize data, analytics and AI. the company is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark, Delta Lake and MLflow. To learn more, follow the company on Twitter, LinkedIn and Facebook. At the company, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here. Our Commitment to Diversity and Inclusion At the company, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at the company are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

Head of AI Solutions, COO Technology - MD (C16) page is loaded Head of AI Solutions, COO Technology - MD (C16)Applylocations: London United Kingdomposted on: Posted Todayjob requisition id: Citi's COO Technology organization is building one of the most consequential AI platforms in global financial services - and this is the role that leads it.The Head of AI Solutions is a newly created, executive-level position with a clear mandate: architect a unified AI strategy, build a world-class engineering team, and deliver production-grade AI at scale across the operational nerve center of a global bank. This is not a coordination or advisory role. It is a builder's role - one with the budget, the mandate, and the organizational reach to make it real.You will own the AI strategy and delivery capability across a $200M+ technology portfolio spanning some of the most operationally complex domains in banking: KYC, fraud detection, wholesale lending operations, global reconciliations, cash management, payments control, non-financial regulatory reporting, payroll, and international operations . The scale is significant, the problems are highly complex at this stage, and the impact is direct - the solutions you build will influence how trillions of dollars in transactions flow daily, how regulatory risk is managed, and how Citi's operational infrastructure evolves over the next decade.Unlike a role at a pure-play technology company, you will be solving AI challenges where failure has regulatory and systemic consequence - and where success reshapes the economics and resilience of critical global operations. The ambiguity is real, the stakes are high, and the opportunity for lasting impact is unmatched.This role reports directly to the Head of COO Technology. Responsibilities: AI Strategy & Platform Architecture: Define and own the multi-year AI strategy for the COO Technology portfolio; translate business intent into a concrete, milestone-driven execution roadmap with measurable outcomes Develop architecture blueprints and end-to-end systems design for Generative AI and agentic workflows across diverse operational domains Build the shared AI platform - reusable models, tooling, guardrails, evaluation frameworks, and accelerators - that reduces duplication, lowers cost, and enables faster adoption across COO Establish and enforce engineering standards, architectural guardrails, and development patterns across all AI initiatives Define a rigorous total cost of ownership model for developing, deploying, and sustaining AI in production Identify and evaluate emerging GenAI technologies, foundation models, and agent frameworks - and make deliberate, defensible decisions on where to build, buy, or partner Production AI Delivery at Enterprise Scale Lead end-to-end delivery of AI solutions across high-complexity, regulated operational environments - from architecture through production deployment, monitoring, and continuous improvement Drive the agentic product development lifecycle: runtime harness design, evaluation frameworks, human-in-the-loop workflows, feedback loops, and production readiness criteria Manage cross-functional delivery spanning engineering, product, data, architecture, cyber, risk & compliance, and operations Proactively identify and resolve dependencies, critical path risks, and systemic delivery blockers; ensure on-time, on-budget execution Ensure all AI solutions meet production-grade standards: stability, scalability, auditability, explainability, and regulatory compliance Executive Partnership & AI Governance Serve as the senior AI executive point of contact for COO function leads - partnering directly with the Heads of Core Operations Technology, Shared Services Technology, and Controls Technology Lead AI governance forums and Architecture Review Boards; establish clear decision rights and review cadences across the portfolio Translate complex technical realities into clear, compelling narratives for senior non-technical audiences - including COO, CIO, and regulatory stakeholders Develop executive-level communications - steering committee materials, portfolio dashboards, and milestone tracking - that improve decision velocity and reduce execution risk Ensure full adherence to Citi's internal policies, risk and control frameworks, model risk management (MRM) standards, and applicable regulatory requirements Building the AI Engineering Organization Build, structure, and lead a high-performing AI engineering function aligned to COO's operational priorities - including team topology, operating model, and career pathways Foster a culture of technical excellence, intellectual curiosity, and pragmatic innovation: engineers who ship production solutions, not prototypes Own and manage the AI technology portfolio budget ( $200M), driving disciplined funding allocation, financial transparency, and cost-to-serve accountability Lead productivity and efficiency programs with measurable targets across cycle time, quality, throughput, and engineering leverage Define and optimize vendor and partner strategy, including strategic AI platform partnerships (e.g., Google, Anthropic), third-party tooling, and outsourced delivery models Qualifications: 15+ years of experience in Technology - Required: Generative AI & LLM Engineering: Deep, hands-on expertise in large language models including model selection, fine-tuning, prompt engineering, retrieval-augmented generation (RAG), vector database design, and evaluation methodologies. You understand how models behave in production, not just in demos. Agentic Systems Design:Proven experience designing and deploying multi-agent architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool-use patterns, human-in-the-loop workflows, and agentic safety at enterprise scale AI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such as MLflow, Vertex AI, or SageMaker Cloud AI Platforms: Demonstrated deployment of AI workloads on AWS, GCP, and/or Azure including managed ML services, scalable inference infrastructure, and vector stores Programming & Frameworks: Strong Python proficiency; working knowledge of PyTorch or TensorFlow; applied experience with AI application frameworks (LangChain, LlamaIndex, or equivalents) Enterprise AI Architecture: Designing AI systems for regulated production environments: data security, model explainability, audit logging, access controls, and integration with legacy systems Leadership & Delivery - Required: 15+ years in technology, with a proven record of leading large-scale engineering organizations through build-out and transformation 10+ years of management experience, including direct leadership of senior engineers and architects, and management of managers across global teams Demonstrated delivery of enterprise AI solutions with realized measurable business outcomes - not just successful pilots or proofs of concept Experience managing large, complex technology budgets ($50M+) with accountability for financial transparency and ROI Track record of operating effectively in matrixed, cross-functional organizations at the intersection of technology and operations Domain & Contextual Knowledge - Strongly Preferred Deep familiarity with financial services operations and the regulatory landscape - particularly KYC/AML, fraud, reconciliations, and regulatory reporting Strong understanding of model risk management (MRM) and what it takes to move AI from development to production under regulatory scrutiny Experience engaging with strategic technology partners (cloud providers, AI platform companies) at an executive level Leadership Profile You build platforms, not point solutions - you instinctively seek the reusable, the shared, the scalable You are equally credible in a deep technical architecture review and a board-level strategy discussion You attract, develop, and retain strong technical talent - engineers want to work for you because they grow You operate with clarity and urgency in ambiguous environments; complexity energizes rather than paralyzes you You communicate with precision - you can make a complex AI architecture concept land with a CRO, a COO, and a principal engineer, and you do it differently for each Education: Bachelors required; Master's in CS, AI/ML, or related field preferred What success looks like: In the first year, we expect the successful candidate to: Establish the AI engineering team and operating model - hire and structure a high-performing team with clear roles, responsibilities, and a strong culture Deliver 3+ production-grade agentic AI systems across priority COO domains, with measurable operational impact (cost, speed, quality, or risk reduction) Launch the shared AI platform - reusable RAG infrastructure, evaluation frameworks, and common tooling adopted across COO Technology Define and align the multi-year AI roadmap with COO function leads and the Head of COO Technology, with clear prioritization, milestones, and funding allocation Establish AI governance - Architecture Review Boards, model risk processes, and compliance frameworks embedded in the delivery lifecycle Why this role Scale that is rare. You will build AI capabilities across one of the world's most operationally complex banking platforms - with direct, measurable impact on how trillions of dollars in transactions are processed, controlled, and reported daily. Greenfield mandate. The team, the platform, and the strategy are yours to define. You will set the architectural direction, the engineering culture, and the standards that govern AI across the COO portfolio. Uniquely hard problems . click apply for full job details

Senior Specialist Solutions Engineer (AI/ML) Location: London, United Kingdom - Hybrid As a Senior Specialist Solutions Engineer (SSE), ML Engineering, you will be the trusted technical ML expert to both Databricks customers and the Field Engineering organisation. You will work with Solution Architects to guide customers in architecting production grade ML applications on Databricks, while aligning their technical roadmap with the evolving Databricks Data Intelligence Platform. You will continue to strengthen your technical skills through applying the latest technologies in GenAI, LLMOps, and ML, while expanding your impact through mentorship and establishing yourself as an ML expert. You will be reporting to the Manager, Field Engineering (Specialist Team). The impact you will have Lead the architectural design of production grade ML workloads on our unified platform, encompassing the entire MLOps lifecycle from end to end pipeline creation and optimization (training/inference) to seamless integration with cloud native services. Provide advanced technical support to the Solution Architects during the technical sales cycle by building MVPs, leading deep dive technical sessions, and strategically aligning ML/data science solutions to complex customer business challenges using relevant real world examples. Serve as the trusted technical advisor for customers developing GenAI solutions, specializing in the design and implementation of RAG architectures on enterprise knowledge bases, enabling natural language querying of structured data, and establishing content generation and monitoring frameworks. Drive community growth and platform adoption through thought leadership activities, including the creation of technical tutorials and training materials, as well as leading hackathons and presenting at industry conferences. What we look for Experienced, technical, customer facing, and with a background in Data Science / Machine Learning, and Data Engineering. Looking to learn and develop in a customer facing technical role as a subject matter expert (SME) in a pre sales environment. Pre sales or post sales experience working with external clients across a variety of industry markets. Data Science/ML Skills Hands on industry ML experience in at least one of the following: ML Engineer: Develop production grade cloud (AWS/Azure/GCP) infrastructure that supports the deployment of ML applications, including drift monitoring. Data Scientist: Experience with the latest techniques in natural language processing, including vector databases, fine tuning LLMs, and deploying LLMs with tools such as HuggingFace, Langchain, and OpenAI. Hands on experience working with Distributed Spark based systems. Experience communicating and teaching technical concepts to non technical and technical audiences alike. Passion for collaboration, life long learning, and driving our values through ML. Preferred 2+ years customer facing experience in a pre sales or post sales role. Preferred Experience working with Apache Spark to process large scale distributed datasets. Can meet expectations for technical training and role specific outcomes within 3 months of hire. Can travel up to 30% when needed. Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region click here. Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio economic status, veteran status, and other protected characteristics. Compliance If access to export controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone.

JOB DESCRIPTION At the company, we understand that customers seek exceptional value and a seamless experience from a trusted financial institution. That's why we launched Chase UK to transform digital banking with intuitive and enjoyable customer journeys. With a strong foundation of trust established by millions of customers in the US, we have been rapidly expanding our presence in the UK and soon across Europe. We have been building the bank of the future from the ground up, offering you the chance to join us and make a significant impact. Job Responsibilities Partner with the engineering lead to set the AIE product vision and multi year strategy, aligned to ICB and firmwide objectives, and own the product roadmaps that translate it into measurable outcomes across both AIE sub teams. Ensure the platform roadmap and the internal AI applications roadmap stay aligned, so that capabilities built by the platform team are validated and consumed by the applications team, and the applications team's needs inform platform priorities. Author clear, detailed product requirements documents (PRDs) and own the backlog in Jira in close collaboration with engineering leads, decomposing initiatives into well defined stories with crisp acceptance criteria, maintaining priority, and making data informed trade offs across scope, schedule, and risk. Lead discovery with platform users and partners across ICB to define capabilities that improve developer experience, reliability, time to production, and compliance. Partner with delivery leads to ensure architectures are scalable, reliable, and operationally sound, and that capabilities ship with evaluation, observability, and clear ownership. Embed technology controls and regulatory requirements into product design, so that teams meet their obligations by adopting platform tooling rather than rebuilding controls bespoke. Drive adoption of platform capabilities through clear documentation, enablement, and internal go to market, demonstrating measurable value to use case teams. Define and track product KPIs across AIE, and use insights for continuous improvement. Coordinate cross team dependencies, remove impediments, and align stakeholders; resolve competing priorities with clear, evidence based communication. Establish high standards for product artifacts and decision quality, and work with Engineering peers to raise velocity and impact. Required Qualifications, Capabilities & Skills Senior product management experience for technical platform or infrastructure products, delivering complex roadmaps in regulated environments with minimal oversight. Strong understanding of the machine learning lifecycle and what it takes to move models and AI applications from experiment to production. Outstanding stakeholder management and influence; builds trust through transparency and effective use of data; adept at resolving ambiguity and competing priorities. Deep partnership with Engineering; fluent in discussing technical trade offs with both technical and non technical stakeholders. Strong analytical and decision making skills; comfortable using metrics and telemetry to guide priorities and measure outcomes. Robust risk and controls mindset; integrates regulatory and technology control requirements into product strategy and execution. Excellent written and verbal communication skills in English. Preferred Qualifications, Capabilities & Skills Product management experience for AI/ML, MLOps, or data platforms. Familiarity with MLOps concepts and frameworks, such as MLflow, Amazon SageMaker, and feature stores. Familiarity with AI concepts, such as Retrieval Augmented Generation (RAG), agentic system architectures, evaluation of LLMs and agents, and Model Context Protocol (MCP). Hands on experience with or solid understanding of Amazon Web Services (AWS). Understanding of cloud architecture and the software development lifecycle. Experience coaching and mentoring other product managers and setting a high bar for execution. Experience operating at scale in highly available, secure environments. Equal Opportunity Employer We recognize that our people are our strength and the diverse talents they bring to our global workforce are directly linked to our success. We are an equal opportunity employer and place a high value on diversity and inclusion at our company. We do not discriminate on the basis of any protected attribute, including race, religion, color, national origin, gender, sexual orientation, gender identity, gender expression, age, marital or veteran status, pregnancy or disability, or any other basis protected under applicable law. We also make reasonable accommodations for applicants' and employees' religious practices and beliefs, as well as mental health or physical disability needs. Visit our FAQs for more information about requesting an accommodation.

Citi's COO Technology organization is building one of the most consequential AI platforms in global financial services - and this is the role that leads it. The Head of AI Solutions is a newly created, executive-level position with a clear mandate: architect a unified AI strategy, build a world class engineering team, and deliver production grade AI at scale across the operational nerve center of a global bank. This is not a coordination or advisory role. It is a builder's role - one with the budget, the mandate, and the organizational reach to make it real. You will own the AI strategy and delivery capability across a $200M+ technology portfolio spanning some of the most operationally complex domains in banking: KYC, fraud detection, wholesale lending operations, global reconciliations, cash management, payments control, non financial regulatory reporting, payroll, and international operations. The scale is significant, the problems are unsolved at this level of complexity, and the impact is direct - the solutions you build will influence how trillions of dollars in transactions flow daily, how regulatory risk is managed, and how Citi's operational infrastructure evolves over the next decade. Unlike a role at a pure play technology company, you will be solving AI challenges where failure has regulatory and systemic consequence - and where success reshapes the economics and resilience of critical global operations. The ambiguity is real, the stakes are high, and the opportunity for lasting impact is unmatched. This role reports directly to the Head of COO Technology. Responsibilities: AI Strategy & Platform Architecture: Define and own the multi year AI strategy for the COO Technology portfolio; translate business intent into a concrete, milestone driven execution roadmap with measurable outcomes Develop architecture blueprints and end to end systems design for Generative AI and agentic workflows across diverse operational domains Build the shared AI platform - reusable models, tooling, guardrails, evaluation frameworks, and accelerators - that reduces duplication, lowers cost, and enables faster adoption across COO Establish and enforce engineering standards, architectural guardrails, and development patterns across all AI initiatives Define a rigorous total cost of ownership model for developing, deploying, and sustaining AI in production Identify and evaluate emerging GenAI technologies, foundation models, and agent frameworks - and make deliberate, defensible decisions on where to build, buy, or partner Production AI Delivery at Enterprise Scale Lead end to end delivery of AI solutions across high complexity, regulated operational environments - from architecture through production deployment, monitoring, and continuous improvement Drive the agentic product development lifecycle: runtime harness design, evaluation frameworks, human in the loop workflows, feedback loops, and production readiness criteria Manage cross functional delivery spanning engineering, product, data, architecture, cyber, risk & compliance, and operations Proactively identify and resolve dependencies, critical path risks, and systemic delivery blockers; ensure on time, on budget execution Ensure all AI solutions meet production grade standards: stability, scalability, auditability, explainability, and regulatory compliance Executive Partnership & AI Governance Serve as the senior AI executive point of contact for COO function leads - partnering directly with the Heads of Core Operations Technology, Shared Services Technology, and Controls Technology Lead AI governance forums and Architecture Review Boards; establish clear decision rights and review cadences across the portfolio Translate complex technical realities into clear, compelling narratives for senior non technical audiences - including COO, CIO, and regulatory stakeholders Develop executive level communications - steering committee materials, portfolio dashboards, and milestone tracking - that improve decision velocity and reduce execution risk Ensure full adherence to Citi's internal policies, risk and control frameworks, model risk management (MRM) standards, and applicable regulatory requirements Building the AI Engineering Organization Build, structure, and lead a high performing AI engineering function aligned to COO's operational priorities - including team topology, operating model, and career pathways Foster a culture of technical excellence, intellectual curiosity, and pragmatic innovation: engineers who ship production solutions, not prototypes Own and manage the AI technology portfolio budget ( $200M), driving disciplined funding allocation, financial transparency, and cost to serve accountability Lead productivity and efficiency programs with measurable targets across cycle time, quality, throughput, and engineering leverage Define and optimize vendor and partner strategy, including strategic AI platform partnerships (e.g., Google, Anthropic), third party tooling, and outsourced delivery models Qualifications: Technological 15+ years of experience in Technology - Required Generative AI & LLM Engineering: Deep, hands on expertise in large language models including model selection, fine tuning, prompt engineering, retrieval augmented generation (RAG), vector database design, and evaluation methodologies. You understand how models behave in production, not just in demos. Agentic Systems Design: Proven experience designing and deploying multi agent architectures and orchestration frameworks (e.g., LangGraph, AutoGen, CrewAI); tool use patterns, human in the loop workflows, and agentic safety at enterprise scale. AI/ML Engineering & MLOps: Full AI/ML lifecycle ownership: training pipelines, model deployment, versioning, monitoring, drift detection, observability (e.g., Weights & Biases, Arize), and lifecycle management using platforms such as MLflow, Vertex AI, or SageMaker. Cloud AI Platforms: Demonstrated deployment of AI workloads on AWS, GCP, and/or Azure including managed ML services, scalable inference infrastructure, and vector stores. Programming & Frameworks: Strong Python proficiency; working knowledge of PyTorch or TensorFlow; applied experience with AI application frameworks (LangChain, LlamaIndex, or equivalents). Enterprise AI Architecture: Designing AI systems for regulated production environments: data security, model explainability, audit logging, access controls, and integration with legacy systems. Leadership & Delivery - Required 15+ years in technology, with a proven record of leading large scale engineering organizations through build out and transformation. 10+ years of management experience, including direct leadership of senior engineers and architects, and management of managers across global teams. Demonstrated delivery of enterprise AI solutions with realized measurable business outcomes - not just successful pilots or proofs of concept. Experience managing large, complex technology budgets ($50M+) with accountability for financial transparency and ROI. Track record of operating effectively in matrixed, cross functional organizations at the intersection of technology and operations. Domain & Contextual Knowledge - Strongly Preferred Deep familiarity with financial services operations and the regulatory landscape - particularly KYC/AML, fraud, reconciliations, and regulatory reporting. Strong understanding of model risk management (MRM) and what it takes to move AI from development to production under regulatory scrutiny. Experience engaging with strategic technology partners (cloud providers, AI platform companies) at an executive level. Leadership Profile You build platforms, not point solutions - you instinctively seek the reusable, the shared, the scalable. You are equally credible in a deep technical architecture review and a board level strategy discussion. You attract, develop, and retain strong technical talent - engineers want to work for you because they grow. You operate with clarity and urgency in ambiguous environments; complexity energizes rather than paralyzes you. You communicate with precision - you can make a complex AI architecture concept land with a CRO, a COO, and a principal engineer, and you do it differently for each. Education: Bachelors required; Master's in CS, AI/ML, or related field preferred. What success looks like: Establish the AI engineering team and operating model - hire and structure a high performing team with clear roles, responsibilities, and a strong culture. Deliver 3+ production grade agentic AI systems across priority COO domains, with measurable operational impact (cost, speed, quality, or risk reduction). Launch the shared AI platform - reusable RAG infrastructure, evaluation frameworks, and common tooling adopted across COO Technology. Define and align the multi year AI roadmap with COO function leads and the Head of COO Technology, with clear prioritization, milestones, and funding allocation. Establish AI governance - Architecture Review Boards, model risk processes, and compliance frameworks embedded in the delivery lifecycle. Why this role Scale that is rare click apply for full job details

Senior Director of Machine Learning (Experiences) London, Oxford, Poland, Krakow About Tripadvisor The Tripadvisor Group connects people to experiences worth sharing, and aims to be the world's most trusted source for travel and experiences. We leverage our brands, technology, and capabilities to connect our global audience with partners through rich content, travel guidance, and two-sided marketplaces for experiences, accommodations, restaurants, and other travel categories. The subsidiaries of Tripadvisor, Inc. (Nasdaq: TRIP), include a portfolio of travel brands and businesses, including Tripadvisor, Viator, and TheFork. Tripadvisor Experiences, a division of Tripadvisor, is the leading marketplace for travel experiences. We believe that making memories is what travel is all about. And with 400,000+ travel experiences to explore-everything from simple tours to extreme adventures (and all the niche, interesting stuff in between)-making memories that will last a lifetime has never been easier. With industry- leading flexibility and last-minute availability, it's never too late to make any day extraordinary. The role As the Senior Director, Machine Learning, you will spearhead our Machine Learning, Data Science and Experimentation efforts, reporting to the Vice President of Engineering. You will play a pivotal role in shaping our ML and AI strategy and ensuring we leverage our data assets to their full potential to drive growth and competitive advantage. This spans the personalization, recommendation, and ranking systems that power how travelers discover and book experiences; rigorous experimentation; and the responsible application of generative AI - all built on our rich first-party data. You will run your organization as a hub-and-spoke: a strong central team that owns talent, craft standards, career growth, tooling, and long-horizon ML strategy, with scientists deployed close to the product areas they serve so delivery stays fast and grounded in real business context. You will be a strategic leader and a credible technologist: a player-coach who sets technical direction, holds the scientific bar, and stays close enough to the work to earn the trust of a demanding team. You will lead and grow the organization through a layer of managers as it scales. What you'll do Leading through a team of managers and senior scientists, you will: Build and lead a unified ML organization. Consolidate and grow ML scientists currently embedded across engineering into one hub-and-spoke model; define the operating model, decision rights, and service commitments so product areas keep momentum through the change. Own the personalization strategy. Set the technical direction for the recommendation, ranking, and search-relevance systems that personalize the traveler journey - multi-objective ranking, sequential and session-based recommenders, embeddings and semantic retrieval, and real-time features - the models that drive discovery and conversion at scale. Lead data science and experimentation. Own predictive modeling, causal inference, and the experimentation platform and standards - A/B testing, guardrail metrics, and rigorous measurement of incremental business impact - as a decision-science partner to Product and the business, championing a strong experimentation culture across the organization. Shape our generative-AI direction. Own the strategy for applying LLMs and generative AI across the Experiences product - conversational trip planning, retrieval-augmented generation grounded in our first-party reviews and content, and automated content - with rigorous evaluation to control quality and cost, including agentic and LLM-driven experiences as this work scales. Grow, develop, and retain the team. Shape ML and data-science career ladders and dual technical/management tracks; partner with Talent Acquisition to attract and hire top-tier ML and data-science talent; calibrate hiring and promotions; establish standards for the research-to-production model lifecycle; and develop both the management track and the senior IC bench - Staff and Principal Scientists. Own production ML health. Set and enforce the standards for reliable, well-monitored, cost-effective models in production - deployment, retraining, monitoring and governance - and the service levels (SLAs) of the ML organization. Partnering with MLOps infrastructure team. Partner across the company. Translate commercial strategy into an ML roadmap with Product, Engineering, Design, and Analytics leadership; represent ML in executive planning; and connect model performance to business outcomes. What you'll bring: Substantial experience in machine learning, together with significant experience leading managers and senior technical teams in a complex, scaled organisation. A track record of shipping production machine learning at scale - ideally personalization, recommendations, search/ranking, or similar - to large consumer traffic, with measurable business impact. Experience building, scaling, or restructuring an ML or data-science organization, and the change-management skill to consolidate and align teams without disrupting delivery. The ability to operate as an executive peer - influencing Director- and VP-level partners across Product and Engineering and shaping strategy, not just execution. A strategic player-coach's technical depth: current enough in modern ML, experimentation, and MLOps to set direction, review architecture, and hold the scientific bar. Strong grounding in experimentation and statistics - A/B testing, causal inference, and rigorous measurement of impact. An MS or PhD in a quantitative field (Computer Science, Statistics, Mathematics, or related), or equivalent depth of applied experience. Excellent communication - the ability to translate complex ML concepts into business decisions for technical and non-technical audiences alike. Job Location: Hybrid - UK (London/Oxford) or Poland (Krakow) This role is a hybrid position that requires 2 days per week in our office What We Offer Competitive compensation packages (routinely benchmarked against the latest industry data), including base salary and annual bonuses "Work your way" with flexibility to suit your lifestyle. Tripadvisor Group takes a remote-friendly approach to collaboration across a worldwide team, with the option to join on-site as often as you'd like or as required by your team. Flexible schedule. Work-life balance is ingrained in our culture by design. Trust and accountability make it work. Donation matching. Give back? Give more! We match qualifying charitable donations annually. Tuition assistance. Want to level up your career? We love to hear it! Receive annual support for qualified programs. Lifestyle benefit. An annual benefit to spend on yourself. Use it on travel, wellness, or whatever suits you. Travel perks. We believe that travel is employee development, so we provide discounts and more. Employee assistance program. We're here for you with resources and programs to help you through life's challenges. Health benefits. We offer great coverage and competitive premiums. Generous referral scheme. Help us grow and be rewarded with generous awards for referring successful candidates. We exist to create value for our customer, the traveler. We enable our suppliers and partners to unlock this value. Their collective behaviors and insights are what drives us. Execution is our edge We act fast, experiment, learn from failure, iterate, and improve the solutions of tomorrow across every aspect of our business. Our execution is agile, data-driven, prioritised, and built to scale. We assume no problem is someone else's problem and finish what can be done today, knowing tomorrow will bring fresh challenges. We succeed together The best outcomes are driven by empathic, humble, and diverse subject matter experts working toward shared goals. We collaborate relentlessly, challenge assumptions, give actionable feedback, and set each other up for success through empowered teams with a clear charter. We transparently take ownership of our growth, individually and as a team. We celebrate the quality of our effort, our learnings, and our collective achievements. We strive to create an accessible and inclusive experience for all candidates. If you need a reasonable accommodation during the application or the recruiting process, please make sure to reach out to your individual recruiter or our team at . If you have any additional questions about careers at Tripadvisor you can email us at . We have all the answers! Massachusetts Notification It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

The Head of Security Architecture with deep expertise in AI systems to design, implement, and maintain secure architectures for enterprise security, artificial intelligence and machine learning initiatives across the organisation. This role will define technical security standards, reference architectures, and implementation frameworks that protect traditional IT environments and AI systems, models, data pipelines, and deployments throughout their life-cycle - from conception to production. The Head of Security Architecture will partner with stakeholders and internal teams to establish a comprehensive, forward-looking security strategy and architecture that enables organisational ambitions while managing AI-related security risks. What you'll be doing What will your essential responsibilities include? Work with stakeholders and internal teams to define a comprehensive, forward-looking Security strategy, inclusive of AI Security and Architecture that supports the organisation's global growth ambitions, incorporating policies, standards, and control frameworks applicable across multiple jurisdictions. Establish and evolve governance mechanisms that promote responsible, ethical, and compliant AI use worldwide, considering regional regulatory nuances and cultural sensitivities. Integrate AI security requirements into security and enterprise architecture, software development life cycles, and model life-cycle management, ensuring consistency and agility across diverse operational regions. Will be part of Security Strategy development and does require interaction with executive business leaders for a global organisation. Architecture & Design: Design enterprise-wide security architectures and reference implementations Establish secure AI development, training, and deployment pipelines Define AI & security data governance and access control frameworks for systems Create security-by-design principles for AI model development Design threat models specific to AI/ML systems and attack surfaces Technical Security Standards: Develop and maintain security frameworks and best practices Create secure coding standards for ML engineers and data scientists Establish model validation, testing, and verification protocols Define encryption, authentication, and authorisation standards for systems Document security requirements and technical specifications Infrastructure & Systems: Architect secure AI infrastructure (cloud, on-prem, hybrid) Design model registry, versioning, and deployment systems with security controls Implement monitoring, logging, and anomaly detection for AI systems Oversee secure data pipelines and feature stores Design isolation and sandboxing for high-risk AI workloads Risk & Threat Mitigation: Conduct threat modelling for AI systems (adversarial attacks, model poisoning, data poisoning) Design defences against AI-specific vulnerabilities Implement model robustness testing and validation frameworks Create incident response architectures for AI security breaches Design recovery and rollback mechanisms for compromised models Vendor & Third-Party Management: Evaluate and architect integrations with third-party AI platforms securely Define security requirements for AI vendors and SaaS providers Design secure APIs and interfaces for external AI model access Oversee security architecture of outsourced AI development Governance & Compliance: Align security architecture with regulatory requirements (GDPR, AI Act, industry standards) Design audit trails and compliance monitoring systems Document architecture decisions and security trade-offs Collaborate on responsible AI and ethical AI architecture considerations Team Leadership & Knowledge Sharing: Lead AI security architecture reviews and design sessions Mentor security engineers and architectsPresent architecture recommendations to technical and executive stakeholders Build and maintain security architecture documentation Drive adoption of secure architecture patterns You will report to Chief Security Officer. What you'll bring We're looking for someone who has these abilities and skills: Substantial experience in cyber security, systems architecture, or infrastructure security Proven capabilities in designing and architecture large-scale systems Experience in hands-on and applied experience with AI/ML security architecture Deep technical knowledge of: Machine learning systems and workflows AI/ML vulnerabilities and attack vectors (adversarial examples, model extraction, poisoning) Cloud security and containerisation (Docker, Kubernetes) Data security, encryption, and access controls API security and micro-services architecture Robust background in threat modelling and security architecture frameworks Experience with secure software development life-cycle (SSDLC) Advanced degree in Computer Science, Cyber security, or related field (or equivalent) Key Competencies Executive judgement and credibility Global risk thinking and cultural agility Governance and control design Influencing and stakeholder management without direct authority Clear, compelling communication tailored for diverse audiences, including boards and regulators Balancing innovation with disciplined risk management on a global scale Preferred Skills CISSP, CCSK, CISM, AAISM or similar security architecture certification Experience with MLOps/MLSecOps platforms Background in machine learning engineering or data science Experience with AI governance and responsible AI frameworks Knowledge of AI-specific security standards (NIST AI RMF, ISO/IEC 42001) Experience in regulated industries (finance, healthcare, government) Contributions to open-source security or AI security projects Speaking/publishing on AI security topics What we offer Inclusion AXA XL is committed to equal employment opportunity and will consider applicants regardless of gender, sexual orientation, age, ethnicity and origins, marital status, religion, disability, or any other protected characteristic. At AXA XL, we know that an inclusive culture and enables business growth and is critical to our success. That's why we have made a strategic commitment to attract, develop, advance and retain the most inclusive workforce possible, and create a culture where everyone can bring their full selves to work and reach their highest potential. It's about helping one another - and our business - to move forward and succeed. Five Business Resource Groups focused on gender, LGBTQ+, ethnicity and origins, disability and inclusion with 20 Chapters around the globe. Robust support for Flexible Working Arrangements Enhanced family-friendly leave benefits Named to the Diversity Best Practices Index Signatory to the UK Women in Finance Charter Learn more at AXA XL is an Equal Opportunity Employer. Total Rewards AXA XL's Reward program is designed to take care of what matters most to you, covering the full picture of your health, wellbeing, lifestyle and financial security. It provides competitive compensation and personalized, inclusive benefits that evolve as you do. We're committed to rewarding your contribution for the long term, so you can be your best self today and look forward to the future with confidence. Sustainability At AXA XL, Sustainability is integral to our business strategy. In an ever-changing world, AXA XL protects what matters most for our clients and communities. We know that sustainability is at the root of a more resilient future. Our 2023-26 Sustainability strategy, called "Roots of resilience", focuses on protecting natural ecosystems, addressing climate change, and embedding sustainable practices across our operations. Our Pillars: Valuing nature: How we impact nature affects how nature impacts us. Resilient ecosystems - the foundation of a sustainable planet and society - are essential to our future. We're committed to protecting and restoring nature - from mangrove forests to the bees in our backyard - by increasing biodiversity awareness and inspiring clients and colleagues to put nature at the heart of their plans. Addressing climate change: The effects of a changing climate are far-reaching and significant. Unpredictable weather, increasing temperatures, and rising sea levels cause both social inequalities and environmental disruption. We're building a net zero strategy, developing insurance products and services, and mobilizing to advance thought leadership and investment in societal-led solutions. Integrating ESG: All companies have a role to play in building a more resilient future. Incorporating ESG considerations into our internal processes and practices builds resilience from the roots of our business. We're training our colleagues, engaging our external partners, and evolving our sustainability governance and reporting. AXA Hearts in Action: We have established volunteering and charitable giving programs to help colleagues support causes that matter most to them, known as AXA XL's "Hearts in Action" programs . click apply for full job details