63 jobs found

About LoopMe LoopMe is an AI company solving one of advertising's hardest problems: making brand advertising actually measurable - and making it perform. Our platform runs patented machine learning models across billions of consumer signals in real time, optimising campaigns toward outcomes like purchase intent, brand lift, and foot traffic rather than proxy metrics like clicks. The result is 2-5x better performance than industry benchmarks, at scale. We operate a high-load programmatic infrastructure - processing millions of ad requests per second with sub-200ms response times globally. This isn't a layer on top of someone else's stack; it's built from the ground up, in-house, by the team you'd be joining. Founded in 2012 and headquartered in London, we now have 400+ people across 19 cities and have sustained 40% revenue CAGR since 2018. The engineering problems here are real, the ownership is genuine, and the scale is significant. The opportunity You own LoopMe's AI throttling system and lead AI products across the business - a senior role combining technical ownership, product ownership, and commercial partnership. You lead a team of four within LoopMe's 17-person Data Science organisation, working directly with commercial, operations, product, and engineering teams and reporting measurable impact to the SLT. The wider Data Science team - led by Chief Data Scientist Dr Leonard Newnham - operates as a single team across London, Poland, and Ukraine, with a track record of publishing award-winning research in automated bidding. This role reports to the Chief Data Scientist. What you'll do Own LoopMe's AI throttling system end to end, covering roadmap, modelling strategy, release planning, measurement and production performance. Lead and develop a team of four data scientists and ML engineers, setting priorities and maintaining high technical standards. Own the full product lifecycle for AI products, from opportunity identification through launch, monitoring and iteration. Partner with commercial teams and clients to understand their needs and coordinate releases with technical and non-technical stakeholders. Partner with operations teams to embed AI products safely and measurably into day-to-day business processes. Establish clear, evidence-led reporting on progress, risks and measurable commercial impact for senior leadership. Develop practical business recommendations by turning ambiguous commercial problems into testable data science questions. Manage technical quality by reviewing model logic, system design and code where needed. What you'll bring Essential Strong commercial experience in data science, machine learning or applied AI, ideally including production systems at significant scale in an adtech environment. A track record of owning AI/ML products end to end, from initial idea through production release and measured business impact Experience leading, coaching or managing data scientists, ML engineers or closely related technical teams. Deep understanding of experimentation, causal measurement, model monitoring and the practical realities of production ML Strong Python, SQL and data engineering literacy, with the ability to go deep technically when required. Nice to have Experience with adtech, real-time bidding, DSPs, SSPs, throttling, pacing or auction systems Excellent communication skills, able to explain complex technical systems to commercial, operations and senior leadership teams. Experience with Google Cloud, Docker, Kafka, Spark, Airflow, ElasticSearch, ClickHouse or similar production infrastructure Experience owning product roadmaps, release governance or cross-functional delivery across distributed teams and time zones. What we can offer Bonus Hybrid working; meaning you'll spend 3 days a week in our Farringdon office 25 days annual leave, plus the Bank Holidays 1 month work-from-anywhere Annual Wellness Day Health Shield; a cash-back health plan for things like dental, optical, physio and wellbeing Access to Thrive; accessible mental health support all in one app Cycle to work scheme Pension LoopMe Gives Back Day Learning & development support, including internal mobility and bi-annual promotion cycles To apply LoopMe does not accept speculative agency submissions. To apply, please use the button below.

At Gigaton, we're on a mission to cut gigatonnes of carbon emissions from the world's biggest emitting industries (like cement, steel and glass), by building autonomous AI control and optimisation systems that learn and leverage the physics of manufacturing. Our products run heavy industrial plants more efficiently, more stably, and with lower emissions in real time - laying the foundation for the next industrial revolution. We are a team of scientists, engineers, builders, and operators who love hard problems, have high standards, and want to make change happen in the physical world. We care about deep tech, but we care even more about whether it delivers cost and carbon impact in a live plant, with real people, under real constraints. With Gigaton, you'll solve really tough problems in places few people ever get close to, and build something that actually helps the planet. Are you up for the challenge? We're looking for a Principal Cloud Engineer to join our platform team and help us push the boundaries of what AI can do for the planet. Your impact Secure, repeatable deployments across enterprise cloud environments. Increasingly, Gigaton deploy cloud workloads across GCP, AWS, and Azure, so that we can deploy ML Ops and data solutions inside our customer's perimeter. You will help to design, simplify, and improve our entire stack so that we can meet stringent cybersecurity guidelines, while remaining agile. Set the technical bar, and lift up your team mates. As the most senior practitioner in the team, you will be a role model and mentor. You will help your team mates through review, pair programming, and architectural oversight, so that every engineer becomes better for working alongside you. A clear architectural roadmap for cloud and DevEx. Gigaton have ambitious goals, and we need to move quickly. You will be entrusted with the architectural decisions that help our entire engineering organisation ship at speed. You will lead through collaborative work and design documents, to build consensus and plan the work that unblocks and supports our engineers. Your main responsibilities Own key platform components across AWS and GCP, shaping architecture for scalable, secure systems. Deploy and operate our workloads inside customer owned cloud accounts, working directly with their IT and infrastructure teams to navigate requirements and constraints. Assist more junior team members with their development, helping them learn core software engineering skills and best practices. Propose and drive improvements to existing systems and pipelines. What a great fit looks like You have deep experience with AWS or GCP (bonus points for both!). You have a solid understanding of cloud networking. You're fluent in Python and Terraform. You have designed or owned CI/CD pipelines for frequent, safe releases. You love to collaborate with others and are familiar with agile technical practices, including pair programming, mobbing and TDD. You balance good engineering practices with pragmatism: you know when "good enough now" beats "perfect later". We're a startup! You are comfortable with ambiguity and enjoy owning a problem from discovery to delivery. Bonus points: You've worked in industrial, IoT, or similarly complex environments. The ways we like to work We are a collaborative team. We regularly and eagerly pair and work through problems together. We love bouncing ideas off each other! We have regular retrospectives to keep getting better as a team. No blame culture: we support each other when things go wrong and always run post mortems so we never make the same mistake twice. We ship fast. We are proud of our CI/CD pipeline and quality gates, and we review our teammates' work quickly. Our small team regularly deploys over a dozen times a day. Yes, we ship on Fridays. We are hooked on observability: we strive to get visibility over all parts of our system to make incident response as painless as possible and to know what needs to improve. In return for your hard work, we'll give you Equity in the company: When we win, you win. You'll get share options, so you're part of our journey from the inside. Flexible working: We trust you to know how and when you work best and to work that out with your team. 30 days of holiday (plus bank holidays). Rest is productive. Take the time you need to recharge. A generous pension scheme. We're planning for the future in more ways than one. Our Operating Principles Go Gig or Go Home: High Bar, All In. What we do matters to humanity, to our customers and to each other. We hold ourselves to an extraordinarily high bar and bring the urgency this mission requires. Concrete Honesty: Be honest. As concrete forms the foundation of our world, genuine honesty and transparency are the bedrock of our culture. Autonomous Ownership: High agency, high ownership. We build systems that take control and make things better. We do the same: see it, own it, drive it. Cement it with Kindness & Fun: Have fun, be kind. We're here to extend Earth's life, but ours is still limited. We want to enjoy the ride. To see these in full, go to Gigaton's Operating Principles Notion page.

At Anaplan, we are a team of innovators focused on optimizing business decision-making through our leading AI-infused scenario planning and analysis platform so our customers can outpace their competition and the market. What unites Anaplanners across teams and geographies is our collective commitment to our customers' success and to our Winning Culture. Our customers rank among the who's who in the Fortune 50. Coca-Cola, LinkedIn, Adobe, LVMH and Bayer are just a few of the 2,400+ global companies who rely on our best-in-class platform. Our Winning Culture is the engine that drives our teams of innovators. We champion diversity of thought and ideas, we behave like leaders regardless of title, we are committed to achieving ambitious goals, and we love celebratingour wins - big and small. Supported by operating principles of being strategy-led, values -based and disciplined in execution, you'll be inspired, connected, developed and rewarded here. Everything that makes you unique is welcome; join us and let's build what's next - together! Role Overview We are seeking a ML Ops Engineer to join our Platform Engineering team at Anaplan. In this role, you will design, scale, and maintain high-performance MLOps and LLMOps infrastructure supporting our cutting-edge AI-infused scenario planning platform. You will work closely with Data Scientists, ML Engineers, and Cloud Infrastructure teams to streamline model training, deployment, and inference while ensuring optimal GPU utilisation, reliability, and cost-efficiency. Your Impact Provision and manage cloud-native AI/ML infrastructure utilising Kubernetes, Docker, and GPU orchestration frameworks (e.g., NVIDIA GPU Operator, Slurm, or Ray). Automate core platform infrastructure using Infrastructure as Code (IaC) tools like Terraform, Helm, and Ansible. Optimise GPU compute workloads, high-speed networking, and storage for efficient model training and low-latency inference. Build and maintain robust CI/CD and MLOps pipelines for continuous model training, evaluation, packaging, and production deployment. Deploy Large Language Models (LLMs) and generative AI workloads using advanced inference engines (e.g., Triton Inference Server, vLLM, TensorRT-LLM). Enable automated model validation, monitoring for model drift, data drift, and latency bottlenecks. Monitor and optimise cloud spend across high-cost GPU/CPU clusters across AWS, GCP, or Azure. Implement auto-scaling strategies, spot instance policies, and dynamic resource allocation to eliminate infrastructure waste. Establish benchmarking and telemetry to track unit economics and throughput for training and serving AI models. Implement end-to-end observability using tools like Prometheus, Grafana, OpenTelemetry, and Weights & Biases or MLflow. Your Skills Hands-on production experience in DevOps, Site Reliability Engineering (SRE), or Platform Engineering, with some experience dedicated to AI/ML infrastructure. Proven track record of deploying, scaling, and operationalising machine learning models and LLMs in cloud-native production environments. Demonstrated experience managing compute-intensive GPU infrastructure and high-performance computing (HPC) environments. Advanced proficiency in Kubernetes (K8s), Docker, Helm, KubeFlow, and service meshes (e.g., Istio). Hands-on experience with Terraform, Ansible, GitHub Actions, ArgoCD, or Jenkins. Experience with vLLM, Ray, MLflow, LangChain / LangSmith, DeepSpeed, or Hugging Face TGI. Solid background in AWS / GCP / Azure, Kubecost, and GPU cost optimisation techniques. Strong skills in Python, Bash, or Go; deep knowledge of Linux kernel tuning and performance monitoring. Our Commitment to Diversity, Equity, Inclusionand Belonging (DEIB) We believe attracting and retaining the best talent and fostering an inclusive culture strengthens our business. DEIB improves our workforce, enhances trust with our partners and customers, and drives business success. Build your career in a place where diversity, equity, inclusion and belonging aren't just words on paper - this is what drives our innovation, it's how we connect, and it contributes to what makes us a market leader. We believe in a hiring and working environment where all people are respected and valued, regardless of gender identity or expression, sexual orientation, religion, ethnicity, age, neurodiversity, disability status, citizenship, or any other aspect which makes people unique. We hire you for who you are, and we want you to bring your authentic self to work every day! We will ensure that individuals with disabilities are provided reasonable accommodation to participate in the job application or interview process, perform essential job functions, and receive equitable benefits and all privileges of employment. Please contact us to request accommodation.

Our vision is to give everyone the belief they can make their move. We aim to make moving simpler, by giving everyone the best place to turn to and return to for access to the tools, expertise, trust, and belief to make it happen. We're home to the UK's largest choice of properties and are the go-to destination for millions of people planning their next move, reading the latest industry news, or just browsing what's on the market. Location: London (Hybrid, 2 days per week in the office) Reporting to: Data Engineering Manager Help shape the future of data at Rightmove At Rightmove, our vision is to give everyone the belief they can make their move. Millions of people use our platform every month, generating data that powers better experiences for consumers and customers across the property market. We're looking for a Senior Data Engineer II to play a leading role in shaping the data platform that underpins the UK's largest property marketplace. You'll combine deep technical expertise with strategic thinking to build scalable, reliable data infrastructure while influencing how data engineering is practised across the organisation. This is an opportunity to have genuine technical influence, working on platform capabilities that support analytics, machine learning, AI initiatives, and data-driven decision-making at scale. You will be expected to be able to drive forward the transition to AI-Assisted Data Engineering and should have solid experience in this area. What you'll be doing Lead the design and evolution of Rightmove's data platform, making key architectural decisions that support long-term scalability, reliability, and performance Own the architecture, design and build of shared platform capabilities for data ingestion, transformation, orchestration, and processing across batch and real-time workloads. Own the integration strategy across tools such asdbt, Spark and Beam Lead the implementation and adoption of data quality, observability, security, testing and deployment including creating and sharing standards Partner closely with Analytics Engineers, Data Scientists, ML Engineers, and product teams to ensure platform capabilities solve real-world problems Evaluate and introduce new technologies, helping the business adopt modern approaches to data engineering and AI-powered workflows, agents and automations Contribute to the data platform roadmap, identifying opportunities for improvement and influencing future investment decisions. Take ownership of growing the team's technical capability through mentoring, structured feedback, and leading by example, and help engineers at all levels develop What we're looking for Significant technical leadership in data engineering or data platform engineering roles, with a track record of leading complex technical initiatives to completion without needing fully formed requirements Strong architectural expertise across modern data & AI platforms, including data ingestion, processing, storage, and transformation. Genuinely cares for the growth and development of engineers around them, investing in mentoring, creating a culture of technical excellence, and supporting others Hands on experience transitioning to an AI-native Data Engineering landscape A strong Python and SQL engineer with experience usingdbt, Spark and Terraform Strong experience working with GCP (or a comparable cloud platform), Infrastructure as Code, and CI/CD practices. Deep understanding of data storage and modelling principles, including dimensional modelling,lakehousepatterns, and the governance and operational setup of tools likedbt, and applies data security and GDPR principles in platform-level architectural decisions Experience building and operating production-scale data pipelines, balancing performance, reliability, and cost efficiency. A collaborative leadership style, with the ability to influence technical direction, mentor others, and build alignment across teams. Excellent communication skills, with the ability to explain complex technical concepts to both technical and non-technical audiences. Our Approach to AI At Rightmove, we believe software and product are ultimately people problems, and everything we build aims to improve the lives of others. We see AI as a helpful way to create more space for the human side of our work, from understanding real needs to making sure we are solving the right problems in the right way. There is an expectation that you are proactive in exploring how AI can support your own workflow and productivity, and that you approach it with curiosity and an open mind. About Rightmove Our vision is to give everyone the belief they can make their move. We aim to make moving simpler by giving everyone the best place to turn to and return to for access to the tools, expertise, trust and belief to make it happen. We're home to the UK's largest choice of properties, and are the go-to destination for millions of people planning their next move, reading the latest industry news, or just browsing what's on the market. Despite this growth, we've remained a friendly, supportive place to work, with employee still working here! We've done this by placing the Rightmove Hows at the heart of everything we do. These are the essential values that reflect our culture, and include: Wecreatevalue by delivering results and building trust with partners and consumers. Wethinkbigger by acting with curiosity and setting bold aspirations. Wecaredeeply by being real, having fun, and valuing diversity. Wemovetogether by being one team - internally collaborative, externally competitive. Wemakea difference by focusing on delivering measurable impact. We believe in careers that open doors and help our team develop by providing an open and inclusive work environment, offering ongoing training opportunities, and supporting charity fundraising events. And with 88% of Rightmovers saying we're a great place to work, we're clearly doing something right! People are the foundation of Rightmove - We'll help you build a career on it. Rightmove will never discriminate based on age, disability, sex, race, religion or belief, gender reassignment, marriage / civil partnership, pregnancy/maternity or sexual orientation. At Rightmove, we believe that a diverse and inclusive workforce leads to better innovation, productivity, and overall success., We are committed to creating a welcoming and inclusive environment for all employees, regardless of their background or identity, to develop and promote a diverse culture that reflects the communities we serve. By applying, you confirm that you are aged at least 18 or over and that you've read and understood our Privacy Policy , which explains how we handle and protect your personal information during the recruitment process. What we offer Cash plan for dental, optical and physio treatments. Private Medical Insurance, Pension and Life Insurance, Employee Assistance Plan. 27 days holiday plus two (paid) volunteering days a year to give back, and holiday buy schemes. Contributory stakeholder pension. Life assurance at 4x your basic salary to a spouse, family member or other nominated person in your life. Competitive compensation package. Paid leave for maternity, paternity, adoption & fertility. Travel Loans, Bike to Work scheme, Rental Deposit Loan. Charitable contributions through Payroll Giving and donation matching. Access deals and discounts on things like travel, electronics, fashion, gym memberships, cinema discounts and more. We offer hybrid working with a minimum of 2 days in the office. For our roles, such as Field or Home-based positions, different working arrangements apply - full details will be shared during the recruitment process.

Anaplan Inc is seeking a ML Ops Engineer to join our Platform Engineering team. You will design, scale, and maintain high-performance MLOps and LLMOps infrastructure supporting our AI-infused scenario planning platform. You will collaborate with Data Scientists, ML Engineers, and Cloud Infrastructure teams to streamline model training, deployment, and inference while optimizing GPU utilisation, reliability, and cost-efficiency.

Data Scientist Salary: Competitive up to £80,000 Depending on experience & skills Location: Hybrid - 3 days per week in the office - Cardiff Job Reference: J13174 Full UK working rights required - no sponsorship available The Role Our client is a global leader in supply chain risk and compliance technology, helping more than 50,000 organisations protect their people, operations and the planet. Their innovative software helps businesses and contractors stay compliant with critical standards across health and safety, sustainability and ethical behaviour. As a Data Scientist, you'll join a growing, highly skilled team spanning data science, data engineering, AI engineering and DevOps. You'll play a key role in building and deploying machine learning and AI solutions that power business applications and internal processes, helping shape how clients and contractors get to work faster, stay compliant and ultimately come home safely every day. This is an exciting opportunity to work across machine learning, predictive analytics, NLP and Large Language Models, with the chance to integrate innovative AI capabilities including LLMs, MCPs, APIs and enterprise data into production platforms. You'll work closely with Engineering and Product teams in a collaborative Agile environment, turning complex data and technology into solutions that deliver tangible business value. The role will include Designing, developing and deploying machine learning models for risk scoring, compliance prediction, churn analysis and contractor performance analytics Building and deploying LLM-based solutions for text analytics, anomaly detection and automated compliance checks Working with Engineering and Product teams to integrate models into production environments across AWS and Azure Using Microsoft Fabric for data ingestion, transformation and feature engineering Implementing model monitoring, retraining and performance optimisation Translating business requirements into effective technical solutions and communicating outcomes to non-technical stakeholders Working collaboratively within a cross-functional Agile environment Keeping up to date with emerging developments across AI, machine learning and predictive analytics Developing innovative data-driven solutions that improve compliance, safety and business performance Skills Proven commercial experience in Data Science and Machine Learning Strong Python and SQL skills Experience with machine learning frameworks such as TensorFlow, PyTorch and Jupyter Notebooks Strong understanding of predictive modelling and machine learning techniques Experience with NLP and Large Language Models (LLMs) Familiarity with AWS services, including S3, SageMaker and Lambda Experience or knowledge of Microsoft Fabric Understanding of the machine learning model development and deployment lifecycle Strong analytical and problem-solving abilities Excellent communication skills, with the ability to explain complex technical concepts to non-technical stakeholders Bachelor's or Master's degree in Data Science, Computer Science, Statistics or a related discipline A proactive, curious mindset with a passion for emerging AI and ML technologies

Solvo.ai is redefining global container shipping with AI. We build machine learning systems that power intelligent, sustainable, and profitable decision-making for one of the world's most complex industries. Our models help carriers and forwarders price in volatile markets-unlocking measurable impact on every vessel, every route, every day. Our team brings decades of experience across logistics, artificial intelligence, and scaling startups. We focus on developing solutions that are not only innovative but proven in production-transforming the economics of global trade. We're proud of what we achieve and how we work: with professionalism, integrity, and transparency. We are looking for someone to join the team in the next 3 months. The Role We're looking for a skilled and motivated Machine Learning Engineer to join our team and help advance the next generation of Solvo's AI products. This is a hands on role at the intersection of research and production, where your work will contribute directly to how container vessels around the world are priced. You'll collaborate closely with scientists, engineers, and industry experts-designing, building, and deploying models that deliver real value for customers. From data exploration and algorithm development to scalable system design, you'll play a key role across the full ML lifecycle and see your work in action at global scale. If you want to apply cutting edge machine learning to a trillion dollar industry and you're excited by solving meaningful, complex problems, we'd love to hear from you. What You'll Do With guidance from the Head of ML, advance Solvo's decision making and pricing models through innovative ML engineering and research. Design, implement, and deploy production grade ML systems with scalability and robustness in mind. Translate state of the art research (probabilistic modelling, Bayesian methods, optimisation) into customer ready solutions. Work collaboratively within a cross functional team to deliver measurable impact to global shipping customers. Communicate complex ideas clearly to both technical and non technical audiences. Continue developing your own skills and contribute to a supportive culture of learning and innovation. What We're Looking For Familiarity and some experience with machine learning research or applied ML, ideally with expertise in probabilistic models and Bayesian statistics. Experience bringing ML models from prototype to production with supervision. Strong analytical and problem solving abilities, and confidence working across disciplines following established guidance. Advanced degree (MSc/PhD) in Computer Science, Statistics, Applied Mathematics, or a related field - or equivalent practical experience. Clear communication skills and a collaborative mindset. Bonus Points PhD in a relevant field. Experience with large scale data systems or distributed training. Experience with optimisation methods (e.g., linear programming) Familiarity with cloud computing platforms (AWS, GCP, Azure). Prior work in pricing, forecasting, or decision optimisation. Why Join Us Competitive compensation and equity package. Hybrid work setup in London. A collaborative, supportive environment where you'll be challenged and encouraged every day. Career growth opportunities at the intersection of AI and global trade. Commitment to Diversity Solvo.ai is an equal opportunity employer. We celebrate diversity and are committed to creating an inclusive environment where everyone feels valued and supported. At this stage we can only accommodate candidates who already have the right to work in the UK.

Role Overview We are seeking an experienced AI Security Engineer to secure AI, machine learning and Generative AI solutions across enterprise environments. The role will focus on embedding security controls throughout the AI lifecycle and strengthening safeguards for LLMs, foundation models, AI models, data pipelines and AI-enabled applications . Key Responsibilities Perform security assessments and threat modelling for AI, ML, Generative AI and LLM-based solutions. Design and implement AI security controls, guardrails and monitoring capabilities. Conduct AI red-team exercises covering prompt injection, jailbreaks, model manipulation, data leakage and adversarial attacks. Secure model development, training, evaluation, deployment and operational environments. Embed security controls within AI CI/CD pipelines and configuration management processes. Partner with data scientists, AI engineers, architects, governance and risk teams. Define AI security standards, patterns and control requirements aligned with enterprise governance. Analyse emerging AI threats and continuously improve defensive controls and testing approaches. Support secure and responsible adoption of AI technologies across enterprise environments. Required Skills Mandatory 8+ years of relevant professional experience. Strong AI security engineering experience. Strong understanding of AI/ML lifecycle security risks . Experience securing LLMs, foundation models and Generative AI applications . Hands-on AI red teaming experience. Practical AI threat modelling experience. AI vulnerability assessment and security testing experience. Experience with prompt injection, jailbreaks and AI attack techniques . Experience securing AI model development, training, evaluation and deployment. DevSecOps and secure CI/CD experience. Secure configuration management experience. Understanding of AI governance and model risk . Experience implementing AI security guardrails and controls . Cloud security experience. Scripting and automation skills. Strong analytical and problem-solving skills. Strong communication and stakeholder management skills. Desirable CISSP, CCSP, CISM or equivalent. Microsoft, AWS or Google Cloud security certification. AI governance or responsible AI certification. ML security certification. Penetration testing certification. Experience working across UK and offshore multidisciplinary teams.

Senior Data Scientist - Pricing & Machine Learning Contract: Permanent, full-time About Protect Group Protect Group helps businesses improve the customer experience and generate additional revenue through innovative technology. Since 2016, we've grown to support more than 400 partners across 75+ countries, with 12 offices worldwide. Our AI-driven technology integrates with online booking and sales platforms, helping businesses across travel, transport, hospitality, events and financial services offer more flexible, customer-friendly experiences. We're an ambitious, collaborative team that values accountability, fresh thinking and people who take ownership. We move quickly, learn from evidence and work together to solve meaningful problems. The role Pricing machine learning sits at the heart of our business. Our pricing engine dynamically sets protection rates across hundreds of partners and millions of transactions, directly influencing revenue and conversion for our partners. We're looking for a hands-on Senior Data Scientist to take a leading role in developing and improving this capability. Reporting to the Head of Data Science, your primary focus will be pricing optimisation: designing models, running experiments and turning commercial opportunities into reliable production systems. You'll also contribute to our wider machine learning work, including risk modelling and agentic AI systems for refund decision-making and automation. This is a role for someone who enjoys both developing sophisticated models and putting them into production. You'll help shape our technical direction while remaining close to the code and accountable for what we ship. You'll join a flat, high-trust team of data scientists and engineers, working closely with commercial, finance and product teams, as well as the wider technical teams behind our AI platform. What you'll work on Pricing optimisation: Developing and improving dynamic pricing models across partners, products and markets. Experimentation: Designing backtests, online experiments and always-on optimisation approaches to measure the effect of pricing changes. Commercial modelling: Balancing revenue, conversion and customer value within pricing decisions. Risk models: Building models that improve our understanding of claims, refunds and transaction-level risk. Agentic AI systems: Creating graph-based, multi-step workflows using loops, branching, routing, tool use, state management, retries and human-in-the-loop controls. Evaluation: Using regression sweeps, canary testing and LLM-as-judge scoring to determine what is ready to ship. ML infrastructure: Building versioned training pipelines, model registries, scheduled jobs and deployments using Azure ML. What you'll be responsible for Owning the development and continuous improvement of our production pricing models. Turning pricing opportunities into measurable hypotheses, experiments and production changes. Evaluating pricing performance through backtesting, online testing and commercial metrics. Developing Bayesian, multi-armed bandit and other optimisation approaches for dynamic pricing. Working with commercial, finance and product teams to understand pricing performance and recommend action. Building supporting risk models and agentic systems where they improve pricing, refund decision-making or operational efficiency. Applying rigorous offline and online evaluation to everything you build. Shipping reliable models through reproducible training pipelines, versioned artefacts, scheduled jobs, monitoring and alerting. Raising the team's technical standards through code and model reviews, mentoring and the development of reusable patterns. Helping the Head of Data Science shape the team's strategy and identify where investment in machine learning will have the greatest impact. Communicating findings, trade-offs and recommendations clearly to both technical and non-technical audiences. What you'll bring At least five years' experience in data science or machine learning, including responsibility for models running in production. Experience of insurance, protection, travel, fintech, risk modelling or another transaction-led industry. Strong hands-on experience building pricing, revenue optimisation or commercially focused decision models. Expert Python skills, including production-quality code, testing and code review, alongside strong SQL. A solid grounding in machine learning techniques such as gradient boosting, GLMs, Bayesian methods and multi-armed bandits, or comparable experimentation and optimisation methods. Experience designing, running and evaluating pricing experiments using both commercial and statistical measures. The ability to connect model performance with commercial outcomes such as revenue, conversion and risk. Hands-on experience designing, building and optimising agentic systems, particularly graph-based and iterative workflows involving loops, branching, routing, tool use and state management. Experience evaluating LLM applications using approaches such as gold datasets, regression testing, LLM-as-judge scoring and canary testing. Experience with cloud ML platforms - ideally Azure ML, Functions and Blob Storage - or the willingness to transfer your knowledge to Azure. Experience with Git-based workflows, CI/CD and modern agent-assisted development tools such as Claude Code or Codex. The ability to turn an ambiguous business problem into a deployed, monitored solution. Strong commercial judgement and confidence discussing model decisions in terms of revenue and risk. Clear written and verbal communication, including with non-technical audiences. An evidence-led approach and experience mentoring others or providing technical leadership. Awareness of GDPR and international data regulations. Useful, but not essential Causal inference and large-scale experiment design. MLOps, including model registries, artefact versioning and drift monitoring. Streamlit or similar tools for internal applications and dashboards. Experience with claims, refunds or customer operations. You don't need to match every item in this section.

Applied AI Engineer - Defence & Security Location: Newcastle SC Cleared / Eligible The Opportunity Join a forward-leaning engineering team applying cutting-edge AI in real-world, mission-critical environments. This role focuses on integrating AI capabilities into production systems, ensuring they are reliable, secure, and deliver measurable impact. What You'll Do Integrate AI/ML components into applications and operational workflows Design and implement Retrieval-Augmented Generation (RAG) solutions Evaluate LLM performance, identify failure modes, and implement mitigation strategies Test, debug, and improve non-deterministic AI systems Balance AI approaches with deterministic or statistical methods where appropriate Collaborate with engineers, data scientists, and stakeholders to deliver production-ready AI solutions What You'll Need Experience building or integrating AI/ML systems into real-world applications Strong understanding of LLMs, ML models, and core statistical concepts Experience evaluating AI systems and improving reliability and performance Ability to debug complex, non-deterministic behaviours Strong communication skills to explain AI trade-offs and risks Bonus Skills Hands-on experience with RAG architectures and LLM tooling Experience working in regulated or high-assurance environments Familiarity with AI safety, risk, and governance considerations Background in software engineering (Python or similar) Security Requirement SC clearance (or willingness/eligibility to obtain) is required.

A.P. Moller - Maersk is an integrated container logistics company and member of the A.P. Moller Group. Connecting and simplifying trade to help our customers grow and thrive. With a dedicated team of over 80,000, operating in 130 countries; we go all the way to enable global trade for a growing world. We leverage cutting-edge technology to optimize operations, enhance customer experience, and drive business growth. We are seeking a Senior Data Engineer to join our team and play a pivotal role. About The Role At Maersk, we are redefining global logistics through data, platform engineering, and AI-driven innovation. As part of this journey, we are building scalable platforms and intelligent systems that enable faster decision-making, operational efficiency, and seamless integration across the enterprise. As a Senior Data Engineer, Data & AI, you will design, build, and operate scalable data products, pipelines, and analytical foundations that power business insights, critical business capabilities, and AI-enabled decision making. You will work across data engineering, analytics enablement, visualization, and AI/ML engineering, contributing to reliable data pipelines, governed datasets, orchestration frameworks, and data products that enable self service analytics and intelligent automation across the enterprise. The role requires a hands on problem solver who can partner with business stakeholders and product teams to understand requirements, translate business needs into practical data solutions, build quick prototypes where useful, and evolve validated solutions into production ready data & AI products. Key Responsibilities Data Engineering, Pipelines & Orchestration Design, build, and optimize scalable batch and streaming data pipelines using modern data engineering patterns Develop robust orchestration workflows for dependable data ingestion, transformation, quality checks, and downstream consumption Apply strong SQL, Python, and PySpark skills to transform complex data into reliable, reusable, and performant data products SQL, Data Modelling & Analytics Enablement Create well-modelled, trusted datasets that support reporting, visualization, advanced analytics, and AI/ML use cases Enable self service data access and governed consumption by building clear data contracts, documentation, and quality controls Contribute to integrated data foundations that provide consistent, reusable data across business domains and platforms Visualization, BI & Data Product Delivery Partner with analytics and product teams to deliver high quality datasets, dashboards, and visualization ready semantic layers Translate business requirements into scalable data models and consumption patterns for operational and executive insights Support adoption of data products by ensuring performance, usability, reliability, and clear lineage from source to insight AI/ML Engineering Enablement Build data pipelines and feature ready datasets that support machine learning, AI, and GenAI use cases Collaborate with data scientists and AI engineers to productionize models, automate data refreshes, and improve repeatability Apply engineering practices for monitoring, testing, versioning, and operationalizing data and ML workflows Cross Functional Delivery & Architecture Work with Product, Analytics, Platform, Data Science, AI teams, and business stakeholders to clarify requirements and deliver pragmatic end to end data solutions Translate business requirements, user feedback, and problem statements into data models, working prototypes, technical designs, and implementation plans Contribute to data architecture discussions and ensure alignment with enterprise standards, security, and governance expectations Support integrations across cloud and enterprise data ecosystems Operational Excellence Ensure data solutions are reliable, scalable, performant, secure, and production ready Monitor, troubleshoot, and continuously improve pipeline performance, data quality, and platform stability Drive automation, observability, and supportability across data, analytics, and AI/ML solutions Our Ideal Candidate Strong data engineering experience with hands on delivery of scalable data pipelines, data products, and analytics foundations Advanced SQL skills with the ability to design performant queries, data models, and transformation logic Hands on knowledge of Python and PySpark for large scale data processing and automation Curious, hands on problem solver who can engage with business stakeholders to understand the real requirement and deliver practical outcomes Comfortable moving between rapid prototyping and production grade data engineering based on business need Experience enabling visualization, BI, AI/ML, or advanced analytics through trusted and well governed data foundations Familiarity with cloud data platforms, orchestration tools, and distributed data processing patterns Strong ownership mindset and ability to work effectively across teams Required Skills/Experience MS or BS in Computer Science or a science/engineering discipline More than 6 years of experience in data engineering, analytics engineering, or data platform delivery Strong proficiency in SQL, Python, and PySpark is required Experience designing and operating ETL/ELT pipelines, orchestration workflows, data quality checks, and production data products Experience with cloud data platforms, distributed processing, data modelling, and analytics/BI consumption patterns Exposure to AI/ML engineering practices, feature pipelines, model productionization, LLM based applications, or agentic AI patterns will be an added advantage Experience with DevOps and DataOps practices, including CI/CD, monitoring, observability, and incident support Maersk is committed to a diverse and inclusive workplace, and we embrace different styles of thinking. Maersk is an equal opportunities employer and welcomes applicants without regard to race, colour, gender, sex, age, religion, creed, national origin, ancestry, citizenship, marital status, sexual orientation, physical or mental disability, medical condition, pregnancy or parental leave, veteran status, gender identity, genetic information, or any other characteristic protected by applicable law. We will consider qualified applicants with criminal histories in a manner consistent with all legal requirements. We are happy to support your need for any adjustments during the application and hiring process. If you need special assistance or an accommodation to use our website, apply for a position, or to perform a job, please contact us by emailing .

As a Gameplay Engineer and Prototyper in Inception, you will build interactive experiences, blurring the lines between game development and AI research. Your mission is to move beyond traditional game engineering to show what is possible when generative models and agentic workflows become core mechanics. You will be a key bridge between research and design, moving fast to stand up 0-to-1 prototypes, crafting expert pipelines when necessary, and finding novel technical solutions to creative problems. You will work in a cross-functional environment where every project involves significant experimentation, rapid iteration, and close collaboration. Artificial intelligence will be one of humanity's most transformative inventions. At Google DeepMind, we are a pioneering AI lab with exceptional interdisciplinary teams focused on advancing AI development to solve complex global challenges and accelerate high-quality product innovation for billions of users. We use our technologies for widespread public benefit and scientific discovery, ensuring safety and ethics are always our highest priority. We are pushing the boundaries across multiple domains. Our global teams offer diverse learning opportunities and varied career pathways for those driven to achieve exceptional results through collective effort. Responsibilities Design and rapidly implement the technical foundations for gameplay prototypes that utilize agentic workflows and generative models as core mechanics. Build internal tools, scaffolding, and workflows that empower designers and researchers to easily experiment with complex AI pipelines. Lead the integration of small- and large-scale streaming models into game environments, creatively hiding latency and consistency issues inherent to probabilistic models. Partner closely with designers and research scientists to solve "unsolvable" technical bottlenecks in early-stage, design-focused projects. Build compliant pipelines for data managing and model testing where stability matters, while maintaining the speed and flexibility required for rapid game prototyping. Minimum qualifications: Bachelor's degree in Computer Science, Mathematics, a related technical field, or equivalent practical experience. 5 years of experience in one or more programming languages (e.g., C++, Python, or JavaScript). Experience working with cross-functional partners (game designers, artists, or producers) to scope and unblock features. Experience architecting core gameplay mechanics, custom tooling, or greenfield technical systems from scratch. Experience in commercially shipping or publicly releasing a game across its full lifecycle (prototype to release). Preferred qualifications: Experience training ML models or integrating generative AI into consumer-facing products. Experience building high-performance web front-ends or interactive browser-based experiences. Practical knowledge of modern game engines like Unreal, Godot, Unity or proprietary internal frameworks. A track record of contributing to open-source projects or innovative technical experiments.

Datatech Analytics is seeking a Data Scientist to join a global leader in supply chain risk and compliance tech. Cardiff-based, hybrid work with 3 days in the office, allowing collaboration with data science, data engineering and DevOps teams. You'll build and deploy ML/NLP solutions, explore LLMs, and partner with product and engineering to deliver production-ready models across AWS and Azure, improving compliance and safety outcomes.