Lead AI Engineer - AI Adoption & Governance We are looking for a Lead AI Engineer to drive the adoption of AI engineering practices across a large-scale digital environment.
This is a senior hands-on individual contributor role, focused on setting standards, defining guardrails, evaluating emerging AI capabilities and helping delivery teams adopt practical, reusable approaches across the Software Development Lifecycle.
The role combines AI engineering, technical governance, adoption and stakeholder influence, with an initial focus on landscape assessment, evaluation and standards before moving towards a more embedded, forward-deployed model with delivery teams.
Key Responsibilities Lead horizon scanning across the AI tooling landscape and translate emerging capabilities into practical opportunities.
Define and promote AI engineering standards, guardrails and reusable adoption patterns across multiple teams.
Build and test agentic AI workflows, including model selection, prompt and harness engineering, tool integration and reusable skills.
Support the adoption of AI across the SDLC, including test generation, code review, documentation, legacy code understanding and modernisation.
Run structured proofs of value with clear hypotheses, baselines, success criteria and evidence-based outcomes.
Embed with delivery teams to prove approaches in real codebases and transition ownership into delivery.
Build capability through coaching, targeted training, practitioner communities and champions networks.
Work across technical, governance, security and commercial functions to remove recurring adoption blockers.
Engage with suppliers and technology providers to identify reusable approaches and reduce fragmented tooling.
Advise where AI adoption should be constrained, paused or stopped due to security, quality or maintainability risks.
Essential Experience Strong hands-on experience with agentic AI engineering.
Experience building agent workflows, selecting models and integrating agents with tools and services.
Strong prompt engineering and harness engineering experience.
Experience with AI coding and agent tooling such as Claude Code, GitHub Copilot, Codex, Kiro or MCP.
Knowledge of applying AI across the software development lifecycle.
Experience defining engineering standards, guardrails or practices adopted across multiple teams.
Strong influencing skills, particularly where formal authority is limited.
Experience working with suppliers, technology providers and senior stakeholders.
Experience coaching teams or building technical communities of practice.
Desirable Experience Strong software engineering background within large-scale production environments.
Experience with platforms such as AWS Bedrock, Azure OpenAI, AI gateways, MCP gateways or model-routing services.
Knowledge of authentication, model access and cost optimisation.
Experience evaluating AI tools and techniques using structured testing and measurable success criteria.
Understanding of AI evaluation approaches including eval harnesses, regression testing for non-deterministic outputs and LLM-as-judge techniques.
Awareness of security, assurance and governance frameworks such as Secure by Design, DPIAs and data protection requirements.
Reasonable Adjustments:
Respect and equality are core values to us. We are proud of the diverse and inclusive community we have built, and we welcome applications from people of all backgrounds and perspectives. Our success is driven by our people, united by the spirit of partnership to deliver the best resourcing solutions for our clients.
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