Job Description The role This is a new kind of role: part knowledge engineer, part workflow builder, part internal product owner. You will sit behind our Transformation Lead operationally, taking requirements from across the business and turning them into production-ready AI skills that the wider team can reach for on every project. You are not just a prompt engineer. You will understand how the whole system works: how skills connect, how Claude's context window and tool use interact, how to structure knowledge so it can be retrieved reliably. And you will constantly be asking whether what we have is the most efficient way to do it. You report to the Transformation Lead and work closely with discipline leads across strategy, creative, accounts, and development. You will also interface with the broader Enero Group AI community as Orchard's skill-building capability becomes a reference point for the group. What you'll do Skill writing and production This is the core of the role. Write, test, and iterate AI skills: system prompts, workflow logic, tool configurations, and usage documentation, to a high production standard Take requirements from the Transformation Lead and discipline leads, translate them into functional skill specs, build the skill, and validate it works as intended before release Join existing skills together into coherent, efficient systems: not just individual prompts but end-to-end workflows Constantly review what exists and ask: is this the right way to do it? Is this efficient? Does this still reflect how we actually work? Iterate based on real-world use, fixing failure modes, tightening outputs, and updating skills when models or agency processes change Knowledge management and infrastructure Own the AI skills library: structure, versioning, naming conventions, metadata, and access, so every skill is findable and usable across the agency Manage the skills repository (GitHub or equivalent), including version control, branching, and review processes Help build Orchard's organisational memory by documenting how we work, how our knowledge is structured, and where things live, in a form the AI system can actually use Run regular audits to retire outdated skills, flag gaps, and maintain quality standards across the library Quality, testing and performance Build and maintain testing frameworks to validate skills before release Establish and uphold the release standard: the bar every skill must clear before it goes into use Track usage, adoption, and impact across the agency; surface patterns, gaps, and failure modes to the Transformation Lead Maintain a structured feedback loop with teams: discovery before building, ongoing input after deployment Best practice and standards Keep pace with advances in prompt engineering, agentic design, and model behaviour, and bring new thinking back to the agency Maintain Orchard's internal AI skills best practice guide Embed responsible AI principles into every skill: bias checks, output validation, and appropriate human oversight
