iProov
Overview iProov provides science-based biometric solutions that enable the world's most security-conscious organizations to streamline secure remote onboarding and authentication for digital and physical access. Our award-winning liveness technology and iSOC offer resilience against deepfakes and generative AI threats while ensuring scalable user experiences. Trusted by governments and enterprises, including the U.S. Department of Homeland Security, U.K. Home Office, GovTech Singapore, ING, and UBS. This global trust is built on both our technology and the strength of our people. We value diversity, equality and inclusion, and aim to foster a culture where individuals of all backgrounds feel confident to bring their whole selves to work, feel included, and have their talents nurtured. The Role Reports to: Chief Scientific Officer Location: WeWork Waterloo - Hybrid Comp: Negotiable (Base) + Company Performance Bonus (20%) + Share Options + iProov Benefits We are looking for a highly capable and hands-on Senior ML Infrastructure Lead to build and scale the technical foundations that enable machine learning to operate effectively in production. This hybrid leadership role sits across machine learning infrastructure, platform engineering and MLOps. You will be responsible for designing and evolving the systems, tooling, processes and standards that allow ML teams to train, deploy, monitor and improve models reliably, securely and at scale. You will work at the intersection of machine learning, software engineering, data, cloud infrastructure and platform reliability, helping bridge the gap between research and production. This role is ideal for someone who can think strategically about long-term platform capability, while still being technically hands-on enough to solve complex engineering and operational challenges. How you can make an impact Lead the design and evolution of our ML platform, infrastructure and MLOps capability Build and maintain scalable, reliable and secure systems for model training, testing, deployment, monitoring and lifecycle management Develop the infrastructure and tooling that enable ML Engineers, Data Scientists and Researchers to work efficiently and ship models with confidence Design robust workflows for CI/CD, model versioning, reproducibility, experimentation, feature management and release management Own and improve the production environment for machine learning systems, ensuring strong standards for availability, performance, observability and resilience Define and implement monitoring across model and platform layers, including system health, data quality, drift, latency, throughput and cost efficiency Build or optimise internal self-service tooling and platform capabilities to reduce friction for teams working on ML use cases Partner closely with ML, Data, Software and Platform Engineering teams to productionise models and improve the end-to-end ML development lifecycle Support the scaling of infrastructure for both training and inference workloads, including high-throughput, real-time or compute-intensive use cases where relevant Drive best practice in governance, security, compliance, auditability and operational rigour across the ML lifecycle Improve the efficiency and cost-effectiveness of ML systems, including cloud resource usage, compute environments and deployment patterns Mentor engineers and act as a technical leader across ML platform and operations topics Help define the roadmap for ML enablement, ensuring the platform can support current needs while scaling for future growth What we would like to see from you You will have experience working in high growth, fast paced tech-first environments. You are passionate about building and launching quality products that have a positive impact. You're an experienced product leader with a background in security, identity (IAM), or enterprise SaaS. You combine strategic vision with operational rigour, and you're motivated by delivering usable, secure, and elegant solutions to complex technical problems. Proven experience in a senior MLOps, ML Platform, ML Infrastructure, Platform Engineering or Machine Learning Systems role Strong hands-on background in software engineering and cloud infrastructure, ideally with direct experience supporting production machine learning environments Experience building and operating systems that support the full ML lifecycle, from experimentation and training through to deployment and monitoring Strong knowledge of Python and sound engineering principles, including testing, automation and code quality Strong experience with cloud platforms such as GCP Experience with Docker, Kubernetes and modern containerised deployment patterns Strong experience with CI/CD pipelines, infrastructure-as-code and workflow orchestration Experience with tools such as Airflow or similar platform and orchestration technologies Good understanding of model observability, data quality, feature pipelines, lineage and reproducibility Experience designing scalable infrastructure for ML workloads, including training, batch inference and real-time serving Strong appreciation of reliability, security, governance and operational excellence in customer-facing or production-critical systems Ability to operate across both strategic and hands-on technical work Strong communication skills and the ability to work effectively across engineering, product and data teams Nice-to-haves Experience supporting computer vision, deep learning, LLM or other compute-intensive ML workloads Experience with GPU infrastructure, distributed training or high-performance compute environments Familiarity with feature stores, model registries and automated retraining pipelines Experience building internal developer platforms or self-service ML tooling Experience in regulated, high-security or high-availability environments Experience leading or mentoring engineers in a scale-up or high-growth technology business Familiarity with responsible AI, model governance or risk controls in production ML setting Our Culture & Recruitment Process At iProov, we value psychological safety, diversity and inclusion. We are an equal opportunities employer and encourage applications from people of all backgrounds. Our recruitment process focuses on qualifications, competence and suitability for the role. If you need an adjustment for a disability or any other reason during the hiring process, please send a request to
Overview iProov provides science-based biometric solutions that enable the world's most security-conscious organizations to streamline secure remote onboarding and authentication for digital and physical access. Our award-winning liveness technology and iSOC offer resilience against deepfakes and generative AI threats while ensuring scalable user experiences. Trusted by governments and enterprises, including the U.S. Department of Homeland Security, U.K. Home Office, GovTech Singapore, ING, and UBS. This global trust is built on both our technology and the strength of our people. We value diversity, equality and inclusion, and aim to foster a culture where individuals of all backgrounds feel confident to bring their whole selves to work, feel included, and have their talents nurtured. The Role Reports to: Chief Scientific Officer Location: WeWork Waterloo - Hybrid Comp: Negotiable (Base) + Company Performance Bonus (20%) + Share Options + iProov Benefits We are looking for a highly capable and hands-on Senior ML Infrastructure Lead to build and scale the technical foundations that enable machine learning to operate effectively in production. This hybrid leadership role sits across machine learning infrastructure, platform engineering and MLOps. You will be responsible for designing and evolving the systems, tooling, processes and standards that allow ML teams to train, deploy, monitor and improve models reliably, securely and at scale. You will work at the intersection of machine learning, software engineering, data, cloud infrastructure and platform reliability, helping bridge the gap between research and production. This role is ideal for someone who can think strategically about long-term platform capability, while still being technically hands-on enough to solve complex engineering and operational challenges. How you can make an impact Lead the design and evolution of our ML platform, infrastructure and MLOps capability Build and maintain scalable, reliable and secure systems for model training, testing, deployment, monitoring and lifecycle management Develop the infrastructure and tooling that enable ML Engineers, Data Scientists and Researchers to work efficiently and ship models with confidence Design robust workflows for CI/CD, model versioning, reproducibility, experimentation, feature management and release management Own and improve the production environment for machine learning systems, ensuring strong standards for availability, performance, observability and resilience Define and implement monitoring across model and platform layers, including system health, data quality, drift, latency, throughput and cost efficiency Build or optimise internal self-service tooling and platform capabilities to reduce friction for teams working on ML use cases Partner closely with ML, Data, Software and Platform Engineering teams to productionise models and improve the end-to-end ML development lifecycle Support the scaling of infrastructure for both training and inference workloads, including high-throughput, real-time or compute-intensive use cases where relevant Drive best practice in governance, security, compliance, auditability and operational rigour across the ML lifecycle Improve the efficiency and cost-effectiveness of ML systems, including cloud resource usage, compute environments and deployment patterns Mentor engineers and act as a technical leader across ML platform and operations topics Help define the roadmap for ML enablement, ensuring the platform can support current needs while scaling for future growth What we would like to see from you You will have experience working in high growth, fast paced tech-first environments. You are passionate about building and launching quality products that have a positive impact. You're an experienced product leader with a background in security, identity (IAM), or enterprise SaaS. You combine strategic vision with operational rigour, and you're motivated by delivering usable, secure, and elegant solutions to complex technical problems. Proven experience in a senior MLOps, ML Platform, ML Infrastructure, Platform Engineering or Machine Learning Systems role Strong hands-on background in software engineering and cloud infrastructure, ideally with direct experience supporting production machine learning environments Experience building and operating systems that support the full ML lifecycle, from experimentation and training through to deployment and monitoring Strong knowledge of Python and sound engineering principles, including testing, automation and code quality Strong experience with cloud platforms such as GCP Experience with Docker, Kubernetes and modern containerised deployment patterns Strong experience with CI/CD pipelines, infrastructure-as-code and workflow orchestration Experience with tools such as Airflow or similar platform and orchestration technologies Good understanding of model observability, data quality, feature pipelines, lineage and reproducibility Experience designing scalable infrastructure for ML workloads, including training, batch inference and real-time serving Strong appreciation of reliability, security, governance and operational excellence in customer-facing or production-critical systems Ability to operate across both strategic and hands-on technical work Strong communication skills and the ability to work effectively across engineering, product and data teams Nice-to-haves Experience supporting computer vision, deep learning, LLM or other compute-intensive ML workloads Experience with GPU infrastructure, distributed training or high-performance compute environments Familiarity with feature stores, model registries and automated retraining pipelines Experience building internal developer platforms or self-service ML tooling Experience in regulated, high-security or high-availability environments Experience leading or mentoring engineers in a scale-up or high-growth technology business Familiarity with responsible AI, model governance or risk controls in production ML setting Our Culture & Recruitment Process At iProov, we value psychological safety, diversity and inclusion. We are an equal opportunities employer and encourage applications from people of all backgrounds. Our recruitment process focuses on qualifications, competence and suitability for the role. If you need an adjustment for a disability or any other reason during the hiring process, please send a request to
Deerfoot Recruitment Solutions Ltd
Azure AI Cloud Engineer London (Hybrid): Onsite 4:1 WFH £635.63pd Umbrella Rate We're seeking a forward-thinking engineer ready to sit at the absolute intersection of Cloud Infrastructure and Artificial Intelligence within this international banking group. This is a unique opportunity to join a small, dynamic team within a leading financial services institution as they evolve their digital engineering capabilities. You will play a pivotal role in building the secure, scalable foundations that underpin critical banking services, transitioning from traditional cloud functions into a high-impact AI enablement powerhouse. In this hands-on role, you will design and develop bespoke cloud and AI solutions, primarily focusing on Azure while expanding into Oracle Cloud and AWS. You will be the bridge between platforms and software engineering, applying rigorous discipline to deliver automated, sustainable patterns that meet complex regulatory and security standards. Your Key Responsibilities Design & Deliver: Architect and implement cloud and AI-ready solutions on Azure, building repeatable patterns and integration services that support next-generation workloads. Secure AI Architecture: Develop and scale secure infrastructure architectures that allow applications to safely access both cloud-based backend services and on-premises data sources. Enforce Governance & Guardrails: Operationalise strict zero-trust governance controls, including robust API management structures to log every single user prompt and completion for full auditability. Engineering Excellence: Drive automation through Infrastructure-as-Code (Bicep, Terraform) and CI/CD pipelines, while maintaining REST APIs and identity-aware solution patterns. Stakeholder Collaboration: Act as a credible technical bridge between business analysts and senior front-office teams (including electronic trading platforms). You will confidently take business requirements, map out constraints, and articulate complex technical limitations to non-technical stakeholders. Skills & Experience Required Production-Level Azure Infrastructure: Deep hands-on experience with core Azure services, PaaS, and Infrastructure-as-Code (Bicep, ARM, or Terraform). You must be hit-the-ground-running at a senior level; the team does not have the capacity to train on core Azure mechanics. AI Engineering (Beyond the Basics): Real-world enterprise experience designing and deploying solutions centred around agentic workflows, Retrieval-Augmented Generation (RAG), and vectorisation patterns, rather than purely self-taught or personal interest projects. Software Engineering & Tech Stack: High proficiency in Python and PowerShell for building automation frameworks, REST APIs, and identity flows. Production exposure to the OpenAI SDK, GPT models, and Palantir Foundry is highly desirable. Identity & Regulated Security: Exceptional understanding of Entra ID, IAM patterns, and implementing rigorous security controls. Experience within a highly regulated environment such as banking, insurance, or healthcare is essential. Qualifications: You should ideally hold an Azure certification (AZ-104 or higher) This is more than just an infrastructure role; it is a chance to influence a global cloud strategy and gain exposure to multi-cloud environments during a major AI transformation. If you thrive in a results-driven environment and want to make a tangible impact on the future of FinTech, we want to hear from you. If you've held any of these roles or used these technologies/skills, this role could be a great fit: Cloud Engineer, AI Engineer, Azure Architect, Platform Engineer, DevOps Engineer, Python Developer, Azure OpenAI, Bicep, Terraform, Entra ID, Generative AI Integration, and FinTech Cloud Specialist. Deerfoot Recruitment Solutions Ltd is a leading independent tech recruitment consultancy in the UK. For every CV sent to clients, we donate £1 to The Born Free Foundation. We are a Climate Action Workforce in partnership with Ecologi. If this role isn't right for you, explore our referral reward program with payouts at interview and placement milestones. Visit our website for details. Deerfoot Recruitment Solutions Ltd acts as an Employment Business in relation to this vacancy.
Azure AI Cloud Engineer London (Hybrid): Onsite 4:1 WFH £635.63pd Umbrella Rate We're seeking a forward-thinking engineer ready to sit at the absolute intersection of Cloud Infrastructure and Artificial Intelligence within this international banking group. This is a unique opportunity to join a small, dynamic team within a leading financial services institution as they evolve their digital engineering capabilities. You will play a pivotal role in building the secure, scalable foundations that underpin critical banking services, transitioning from traditional cloud functions into a high-impact AI enablement powerhouse. In this hands-on role, you will design and develop bespoke cloud and AI solutions, primarily focusing on Azure while expanding into Oracle Cloud and AWS. You will be the bridge between platforms and software engineering, applying rigorous discipline to deliver automated, sustainable patterns that meet complex regulatory and security standards. Your Key Responsibilities Design & Deliver: Architect and implement cloud and AI-ready solutions on Azure, building repeatable patterns and integration services that support next-generation workloads. Secure AI Architecture: Develop and scale secure infrastructure architectures that allow applications to safely access both cloud-based backend services and on-premises data sources. Enforce Governance & Guardrails: Operationalise strict zero-trust governance controls, including robust API management structures to log every single user prompt and completion for full auditability. Engineering Excellence: Drive automation through Infrastructure-as-Code (Bicep, Terraform) and CI/CD pipelines, while maintaining REST APIs and identity-aware solution patterns. Stakeholder Collaboration: Act as a credible technical bridge between business analysts and senior front-office teams (including electronic trading platforms). You will confidently take business requirements, map out constraints, and articulate complex technical limitations to non-technical stakeholders. Skills & Experience Required Production-Level Azure Infrastructure: Deep hands-on experience with core Azure services, PaaS, and Infrastructure-as-Code (Bicep, ARM, or Terraform). You must be hit-the-ground-running at a senior level; the team does not have the capacity to train on core Azure mechanics. AI Engineering (Beyond the Basics): Real-world enterprise experience designing and deploying solutions centred around agentic workflows, Retrieval-Augmented Generation (RAG), and vectorisation patterns, rather than purely self-taught or personal interest projects. Software Engineering & Tech Stack: High proficiency in Python and PowerShell for building automation frameworks, REST APIs, and identity flows. Production exposure to the OpenAI SDK, GPT models, and Palantir Foundry is highly desirable. Identity & Regulated Security: Exceptional understanding of Entra ID, IAM patterns, and implementing rigorous security controls. Experience within a highly regulated environment such as banking, insurance, or healthcare is essential. Qualifications: You should ideally hold an Azure certification (AZ-104 or higher) This is more than just an infrastructure role; it is a chance to influence a global cloud strategy and gain exposure to multi-cloud environments during a major AI transformation. If you thrive in a results-driven environment and want to make a tangible impact on the future of FinTech, we want to hear from you. If you've held any of these roles or used these technologies/skills, this role could be a great fit: Cloud Engineer, AI Engineer, Azure Architect, Platform Engineer, DevOps Engineer, Python Developer, Azure OpenAI, Bicep, Terraform, Entra ID, Generative AI Integration, and FinTech Cloud Specialist. Deerfoot Recruitment Solutions Ltd is a leading independent tech recruitment consultancy in the UK. For every CV sent to clients, we donate £1 to The Born Free Foundation. We are a Climate Action Workforce in partnership with Ecologi. If this role isn't right for you, explore our referral reward program with payouts at interview and placement milestones. Visit our website for details. Deerfoot Recruitment Solutions Ltd acts as an Employment Business in relation to this vacancy.
