Lead AI Engineer

Job Title: Lead AI Engineer Division: Data, Technology and Innovation Department: AI Product Delivery Salary: National (Edinburgh and Leeds) ranging from £74,900 to £108,000 and London from £82,300 to £120,000 per annum (salary offered will be based on skills and experience) This role is graded as: Technical Specialist – Regulatory Your external recruitment contact is Benjamin via benjamin.paulon@fca.org.uk. Your internal recruitment contact is Lauren via Lauren.Pyrah@fca.org.uk Applications must be submitted through our online portal. Applications sent via social media or email will not be accepted. About the FCA and team We regulate financial services firms in the UK, to keep financial markets fair, thriving and effective. By joining us, you’ll play a key part in protecting consumers, driving economic growth, and shaping the future of UK finance services. The Data, Technology and Innovation (DTI) division enables the FCA to be a digital-first, data-led smart regulator by delivering a secure, agile, and cost-effective technology and data ecosystem that drives better decisions, transparency, and operational efficiency. Working alongside the wider AI Programme (which will continue to oversee/coordinate AI activity across the FCA), the department will partner with business leads to shape and deliver work in priority areas — Authorisations, SPC, EMO and Anti‑Money Laundering. This is a hands-on technical lead role building the FCA's AI Platform. You'll design and build, from the ground up, the core capabilities that every AI product at the FCA relies on: retrieval, memory, agent orchestration, model access, evaluation, guardrails and observability. You'll own the platform codebase and its ongoing development, establishing the technical vision, engineering standards, and quality benchmarks for the team. Role responsibilities Lead the AI Platform build and own the platform architecture. Design and run the FCA's AI Platform on AWS as secure, reusable, self-service capabilities with clear APIs, SDKs and documentation. Set the architecture, roadmap and reference architectures, translate constraints across product, data, security, legal and operations into clear technical standards, take designs through enterprise review, and make evidence-based build-vs-buy and model decisions Deliver a model gateway and access layer. Provide governed access to Amazon Bedrock and other approved models, covering routing, fallback, caching, rate limits, cost tracking and access control Establish retrieval (RAG) as a platform service and engineer AI memory services. Cover ingestion, embeddings, hybrid search, reranking and access-controlled retrieval that cites its sources, ready for teams to plug in their own document sets. Handle context management, session and case memory, and long-term memory, with retention policies that meet data protection requirements Develop the agent runtime (the harness). Cover orchestration, tool calling, MCP integrations, multi-agent workflows, sandboxed execution, approval checkpoints and audit trails Embed guardrails and safety controls, making filtering, prompt-injection defences, personal data redaction and policy enforcement the default Set prompt engineering, evaluation and AI development lifecycle (AI DLC) standards. Define prompt standards and supply tooling for golden datasets, automated evals, regression testing and red-teaming that teams can run against their own solutions. Establish automated, traceable CI/CD with evaluation gates, prompt and model versioning, controlled releases and drift monitoring, aligned with AI Programme governance and covering how engineers use AI coding assistants Drive observability and operations. Implement tracing, dashboards and alerting across quality, latency, cost, safety and reliability, plus runbooks and on-call practice Work closely with stakeholders and lead by example. Partner with the AI Product team and wider stakeholders to support their deliverables and turn recurring needs into platform capabilities. Write reference implementations and reusable patterns, pair with engineers, review code, and raise the engineering standard Skills required Minimum: Extensive hands-on backend engineering experience building production services such as APIs and microservices with solid software engineering fundamentals combined with experience developing technical architecture and designs and taking them through enterprise review processes Hands-on experience building AI applications and/or shared AI platform components, such as AI memory, RAG pipelines, agent harnesses, model gateways and evaluation frameworks, and the ability to explain your design trade-offs and how you measured them Experience delivering cloud solutions on AWS, including deploying, operating and troubleshooting services in live environments and working knowledge of modern DevOps practices such as CI/CD, containerisation and monitoring, with experience collaborating with engineers to deliver iteratively Essential: Experience embedding AI workflows into existing applications in production, giving you a strong sense of what teams need from a platform Practical experience with Amazon Bedrock and the wider AWS AI and platform stack (for example Bedrock Agents or AgentCore, Knowledge Bases, Guardrails, OpenSearch or pgvector, Lambda, ECS/EKS, Step Functions and API Gateway) and with agent orchestration frameworks (for example LangGraph or Strands Agents) Experience establishing or supporting an AI DLC (or equivalent LLMOps practice) within an engineering function, including evaluation gates, release controls and monitoring for AI services Demonstrable skill in prompt and context engineering and in designing evaluation approaches for LLM systems (quality, safety and guardrails, performance), with measurable success criteria Experience designing platform capabilities that other engineering teams consume, such as APIs, SDKs, templates and self-service tooling Experience leading engineers technically (setting patterns and standards, reviewing designs and code, unblocking delivery) while staying hands-on, and experience in a regulated environment (auditability, explainability, model risk, data residency) Experience with LLM evaluation and observability tooling, for example Langfuse, LangSmith, or OpenTelemetry Benefits 28 days annual leave plus bank holidays Hybrid model where employees work a minimum of 40% in the office each month (expectation of 50% for senior leaders). Changing from September to a minimum of 50% in the office each month (expectation of 60% for Directors and Executive Directors) Non-contributory pension (8–12% depending on age) and life assurance at eight times your salary Private healthcare with Bupa, income protection, and 24/7 Employee Assistance 35 hours of paid volunteering annually A flexible benefits scheme designed around your lifestyle For a full list of our benefits, and our recruitment process as a whole visit our benefits page. Our values & culture Our colleagues are the key to our success as a regulator. We are committed to fostering a diverse and inclusive culture: one that’s free from discrimination and bias, celebrates difference, and supports colleagues to deliver at their best. We believe that our differences and similarities enable us to be a better organisation – one that makes better decisions, drives innovation, and delivers better regulation. If you require any adjustments due to a disability or condition, your recruiter is here to help - reach out for tailored support. We welcome diverse working styles and aim to find flexible solutions that suit both the role and individual needs, including options like part-time and job sharing where applicable. Disability Confident: our hiring approach We’re proud to be a Disability Confident Employer, and therefore, people or individuals with disabilities and long-term conditions who best meet the minimum criteria for a role will go through to the next stage of the