The role

We're looking for a unique blend of hands-on software engineer and passionate technical educator to drive our internal AI transformation. You'll turn generic AI capabilities into company-specific leverage - setting up reliable tooling, defining security guardrails, and creating comprehensive learning paths. You'll act as the central bridge between engineering and the broader business, championing AI adoption and shifting workflows across departments.

If you love exploring LLMs, building prototypes, and have a talent for explaining complex concepts in a way that truly empowers others, this role will give you direct impact on the productivity and culture of the entire organisation.

What you'll own

AI Tooling Setup - hands-on installation, configuration, and integration of AI tools (Claude, Copilot, Cursor) with internal systems via MCP servers to build context-aware, reliable AI workflows
Phased AI Adoption - lead rollout starting with engineering teams (complex workflows, agents), then systematically expanding to non-technical departments
Enablement Programmes - create and evolve training curricula, playbooks, workshops, and self-serve documentation to upskill the entire company on LLMs
AI Standards & Best Practices - codify prompting guidelines, build AI-ready documentation, and establish reusable workflow patterns to make AI usage predictable and effective across teams
AI Champion Network - identify, train, and support a community of local AI advocates within various teams to multiply adoption horizontally
Security Guardrails - partner with Security, IT, and Legal to define and implement sensible defaults for data privacy, prompt hygiene, and IP risks without creating unnecessary friction
Tool Evaluation - maintain an evidence-based methodology for selecting AI tools, benchmarked against real internal workflows rather than vendor hype
Impact Measurement - track AI adoption metrics and show where AI creates real leverage and where to invest next
Hands-on Credibility - stay deeply technical by prototyping integrations, dogfooding tools daily, and building reference workflows; your credibility rests on doing the work, not just teaching it
Travel - occasional office visits to run in-person workshops, onboard teams, and collaborate directly with engineers and stakeholders

What you bring

Technical:

5+ years of hands-on software engineering experience - specific tech stack (JS/TS, Python, PHP, Go, Java) matters less than genuine engineering fluency
Working competence in at least one language (Go, JavaScript/TypeScript, or PHP) to build integrations, glue code, and prototypes
1+ year of deep, hands-on experience with LLMs (Claude, GPT, etc.) beyond casual use - you understand prompt design, agentic workflows, and real-world model limitations
Proven experience configuring or building AI-assisted developer tools (AI coding assistants, RAG architectures, agents)
Strong API integration skills (RESTful services, auth, webhooks) and solid Git workflows
Sound understanding of data security, privacy, and access-control basics

Soft skills & mindset:

Exceptional communication - you can explain complex technical concepts to both engineers and non-technical teams; you write playbooks, docs, and training materials people actually use (this is a communication-first role)
Proven experience in mentoring, technical onboarding, running internal talks, or building communities of practice
Influence without authority - you build enthusiasm, drive adoption, and shift behaviours across teams that don't report to you
Comfortable facilitating workshops and presenting to groups
Proactive, highly analytical, and comfortable defining your own roadmap in an ambiguous space
English at C1 level strongly preferred (B2 minimum)

Nice to have:

Hands-on experience with Claude, Cowork, and MCP (Model Context Protocol)
Background in Developer Advocacy, Developer Relations, or formal internal technical enablement
Experience creating formal technical documentation, courseware, or developer-facing content
Familiarity with React/Next.js for building internal tools and demos
Comfort with SQL and exposure to NoSQL or vector databases
Working knowledge of Docker, CI/CD, and containerised environments (Kubernetes a plus)