SC Cleared Lead AI Engineer (AI adoption & Governance)

We are looking for a Lead AI Engineer, specialising in AI Adoption & Governance with demonstrable hands-on practice in current AI engineering tooling with proven experience setting standards and guardrails across the Software Development Lifecycle (SDLC). Fluent in agentic ways of working including model selection, prompt / harness engineering, agent-tool integration (such as MCP), reusable skill and instruction packaging. The role pairs horizon scanning with practical delivery, spotting emerging capability maps and converting isolated experimentation into reusable, centralised adoption patterns. Initial emphasis on landscape setting, evaluation and standard design, shifting over time towards a forward deployed model. Strong stakeholder management is essential, internally across portfolios, professions and assurance functions, and externally with suppliers and technology providers. This is an influencing role rather than a mandating one, so success depends on evidence, brokering and consensus rather than formal authority over portfolio tooling decisions.*This is a senior technical influence role rather than a conventional people-management position. The person should be capable of leading standards and adoption across multiple teams while remaining sufficiently hands-on to test approaches and work directly with delivery teams.

Essential

• Hands-on practice with agentic AI engineering: building and running agent workflows, model selection, prompt / harness engineering, agent-tool integration, reusable skills and instructions.
• Knowledge of applying AI across the SDLC including test generation, code review augmentation, documentation, legacy comprehension, and modernisation.
• AI coding and agent tooling exposure e.g., Claude Code, GitHub CoPilot, Codex, Kiro, MCP server development
• Defined standards, guardrails, or engineering practices that were adopted across multiple teams, with evidence of how adoption was achieved.
• Influenced without formal authority across organisational / team boundaries. Prior engagement with suppliers and technology providers.
• Provided coaching, targeted training or running champions network, supporting a self-sustaining community of practice.

Desirable

• Software engineering background in large-scale production systems, sufficient to embed within delivery teams and be credible on their stack.
• Model access and platform routes e.g., AWS Bedrock, Azure OpenAI, AI / MCP Gateways, model routing, authentication, and cost optimisation
• Structured evaluation of tools or techniques, e.g., defining clear hypotheses, baselines, success criteria, and candid reporting including negative results.
• Knowledge of AI evaluation and measurement processes e.g., eval harnesses, regression suites for non-deterministic output, LLM-as-judge
• Awareness of assurance and security context e.g., Secure by Design, DPIA, DPA

Lead-level individual contributor

This is a senior technical influence role rather than a conventional people-management position. The person should be capable of leading standards and adoption across multiple teams while remaining sufficiently hands-on to test approaches and work directly with delivery teams.

• Lead horizon scanning across the AI tooling landscape, translating emerging capability into practical, prioritised opportunities for portfolios.
• Build and sustain the internal practitioner community: run knowledge sharing, drop-in support and champions networks, actively seeking out teams already experimenting so their learning is captured and reused.
• Build capability through coaching and targeted training so adoption persists without central support.
• Engage across government and industry to bring proven approaches in, contribute Home Office evidence back, and avoid duplication of work.
• Engage suppliers and technology providers to identify reusable practice and reduce fragmented, supplier-specific approaches.
• Surface and broker resolution of reoccurring adoption blockers across governance, tooling, data, security and commercial routes.
• Run structured proofs of value with defined hypotheses, baselines and exist conditions; reporting candidly on benefit realisation.
• Embed with delivery teams as a forward deployed engineer, providing hands-on support to prove approaches in real codebases and transitioning ownership to those teams.
• Advise where AI adoption should be constrained, paused, or stopped based on risk to security, quality or maintainability

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