Montash
Lead AI Engineer (SC Cleared)
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Job Title: Lead AI Engineer (SC Cleared)
Location: Remote (with ad-hoc travel to client’s office in London)
Contract Length: 6 Months (with scope to extend)
Start Date: ASAP
IR35: Inside
Interview Process: 1 Stage, MS Teams
Clearance Required: SC (needs to have been used within the past 10 months)
We are supporting a government client in hiring a Lead AI Engineer to drive AI adoption and governance across a large, security-sensitive engineering organisation. This is a senior technical influence role, pairing hands-on AI engineering practice with the ability to set standards and guardrails adopted across multiple delivery teams.
The successful candidate will need demonstrable, hands-on practice with agentic AI engineering tooling, including model selection, prompt/harness engineering and agent-tool integration, alongside a genuine track record of getting engineering standards adopted across teams without formal authority. Initial emphasis will be on landscape setting, evaluation and standard design, shifting over time towards a forward deployed model working directly within delivery teams.
This role suits an experienced, senior AI/software engineer who has moved deeply into practical AI-enabled engineering, comfortable operating as a Lead-level individual contributor rather than in a conventional people-management capacity, and confident engaging stakeholders across portfolios, professions, assurance functions and external suppliers.
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Key Responsibilities
- Lead horizon scanning across the AI tooling landscape, translating emerging capability into practical, prioritised opportunities for portfolios
- Build and sustain an internal practitioner community, running knowledge sharing, drop-in support and champions networks
- Build capability through coaching and targeted training so adoption persists without central support
- Engage across government and industry to bring proven approaches in, contribute 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 recurring adoption blockers across governance, tooling, data, security and commercial routes
- Run structured proofs of value with defined hypotheses, baselines and exit 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
Essential Skills
- Hands-on practice with agentic AI engineering, including building and running agent workflows, model selection, prompt/harness engineering and agent-tool integration
- Experience defining engineering standards, guardrails or practices that were adopted across multiple teams, with evidence of how adoption was achieved
- Experience influencing without formal authority across organisational or team boundaries, including prior engagement with suppliers and technology providers
- Knowledge of applying AI across the Software Development Lifecycle, including test generation, code review augmentation, documentation and legacy comprehension
- AI coding and agent tooling exposure, for example Claude Code, GitHub Copilot, Codex, Kiro or MCP server development
- Experience providing coaching, targeted training or running a champions network, to support a self-sustaining community of practice
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Desirable Skills
- Software engineering background in large-scale production systems, sufficient to embed within delivery teams and be credible on their stack
- Experience with model access and platform routes, such as AWS Bedrock, Azure OpenAI, AI/MCP Gateways, model routing, authentication and cost optimisation
- Experience running structured evaluations of tools or techniques, including defining hypotheses, baselines, success criteria and candid reporting of negative results
- Knowledge of AI evaluation and measurement processes, such as eval harnesses, regression suites for non-deterministic output and LLM-as-judge approaches
- Awareness of assurance and security context, such as Secure by Design, DPIA and DPA
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