What Success Looks Like

• Sustainable team adoption of agentic development practices, with bootcamp graduates using agentic tooling as a natural part of their daily workflow rather than as an occasional experiment. Embedded assignments have made a measurable impact on both the speed and depth of adoption.
• A documented and actively maintained best practice library, providing practical guidance, reusable patterns, and proven approaches drawn from real-world experience across multiple divisions, teams, and codebases.
• A strong feedback loop between delivery teams and programme leadership, enabling more informed decisions on tooling, training, rollout sequencing, and investment priorities based on real adoption outcomes.
• Increased capability across engineering and agile communities, with Engineering Leads, Agile Coaches, and other stakeholders better equipped to support and sustain agentic ways of working within their teams.
• Clear visibility of adoption maturity across the organisation, including where agentic practices are thriving, where additional support is required, and which interventions will deliver the greatest value in the next phase of the programme.

Future Growth & Impact

• Helping shape and optimise advanced agentic engineering practices, including areas such as multi-agent workflows, automated testing, and agentic code review.
• Identifying and influencing future capability-building and training requirements as teams continue to mature beyond the initial bootcamp phase.
• Contributing to the development of a broader engineering enablement function, supporting the evolution from agentic adoption into wider developer experience (DevEx) and engineering productivity initiatives across the estate.

Experience

• Deep, hands-on experience with agentic development tooling in a professional engineering context — Cursor, GitHub Copilot, Claude Code, or equivalent.
• Not just familiarity, but sustained daily use on real codebases, with a clear point of view on what works and where the limits are.
• Strong software engineering background across at least one primary language and stack. Sufficient breadth to pick up an unfamiliar codebase and be productive within it quickly.
• The role requires genuine engineering credibility, not just knowledge of the tools.
• Experience working across multiple teams or contexts simultaneously, or moving between assignments in a consultative or enabling capacity.
• Comfortable being the new person in a team repeatedly and building trust and credibility quickly.
• A track record of solving real engineering problems - not designing solutions in the abstract, but sitting with a team, understanding their specific situation and working through it with them.
• Familiarity with modern software delivery practices: CI/CD, trunk-based development, automated testing, observability tooling.
• Enough context to understand how agentic workflows fit into - and sometimes challenge - existing engineering norms.

Approach

• Does the work first, explains it second. Earns credibility with engineers by being genuinely useful on real problems, not by presenting frameworks or running workshops.
• Meets teams where they are. Understands that adoption challenges are rarely just technical - they are also about confidence, habit and context - and adjusts accordingly without losing focus on the engineering substance.
• Curious and honest about what agentic tooling can and cannot do. Does not oversell, does not hedge. Has a clear, experience-based view of where AI-assisted development genuinely accelerates and where human judgement remains the right call.
• Shares knowledge actively and generously. Writes things up, contributes to the community, flags patterns to the Director of Agentic AI - because individual impact at team level compounds when it is shared across the estate.
• Comfortable with context-switching and ambiguity. Each embedded assignment brings a different codebase, a different team culture and a different set of adoption challenges. Treats that variety as interesting rather than disruptive.
• Brings a solution-based mindset and helps teams develop the same. Moves the conversation from “this doesn’t work for us” to “let’s figure out why and how to fix it”.

Useful Experience

• Experience with MCP (Model Context Protocol) server configuration, tool integration or agentic workflow automation beyond IDE-level tooling.
• Exposure to prompt engineering, agent orchestration or multi-agent system design at a practical level.
• Experience in a B2B SaaS or multi-product software organisation, particularly one with a mix of modern and legacy codebases.
• Familiarity with the team topologies model and how enabling functions operate within a distributed engineering organisation.
• Experience in regulated or compliance-sensitive software environments (accountancy, payroll, HCM, or education).