AI Engineer

• Enablement & coaching — work directly with engineers through 1:1s, pair programming, workshops, office hours, and cohorts to raise their effectiveness with AI-assisted development: prompting, workflow design, and model/tool selection. Identify under-adoption and lift teams up to the standard.
• Best-practice propagation — capture what works (and what fails) in one team and systematically transfer it: living best-practice guides, prompt libraries, troubleshooting playbooks, and an internal AI knowledge base and skills registry.
• Scale through champions — stand up and support a network of embedded AI champions so enablement scales past a single person.
• Unblocking & feedback loop — be the go-to when engineers get stuck with AI tooling. Proactively tour teams to surface blockers early, aggregate them into patterns, and escalate systemic issues to leadership.
• Internal integrations & tooling — build internal AI integrations and reusable tooling hands-on (Node.js/TypeScript, MCP) — from prototype to production independently, including auth, access control, security review, and CI/CD.
• Evaluation & scouting — run structured evaluations of AI tools, models, and internal integrations; monitor the fast-moving landscape (Claude Code, Cursor, Copilot/Codex, new model releases) and deliver curated, actionable recommendations.
• Measurement & impact — define and track adoption and productivity metrics by team, tool, and individual. Build dashboards on real usage and output — adoption/active-usage rate, assistant acceptance rate, PR throughput and cycle time — with a quality guardrail (change failure rate) so speed never comes at the cost of quality. Reference frameworks: DX Core 4, DORA.
• Cross-functional partnering — partner with security/compliance on safe-by-design guardrails, and with GTM to translate field needs into internal enablement and feed adoption patterns back.

Senior AI Enablement Engineer

• Solve business problems by applying daily AI (LLMs, no-code tools).
• Identify and prioritize use cases, prototype them, and bring them to a working state.
• Ensure the independent use of AI across business functions.
• Define, evaluate, and manage metrics for the effectiveness of daily AI implementation (P&L, customer metrics, employee performance, etc.).
• Drive the development of an internal AI community focused on business challenges.
• Monitor the AI tools market and integrate solutions to address business challenges.

AI Transformation Lead

• Take complete ownership and deliver major AI engineering features within agreed timelines.
• Own AI output quality, structure, and predictability across all user-facing AI interactions.
• Design, implement, and maintain output-type-based AI systems, including segmentation, routing, and enforcement.
• Ensure consistent output structure and formatting across different LLMs for the same request type.
• Integrate and orchestrate multiple LLM providers via OpenRouter, managing model selection, fallback strategies, and cost optimisations.
• Design and orchestrate tool-using and agentic AI workflows, defining clean tool contracts (including MCP-based tools), function-calling interfaces, and reliable AI-to-system integrations.
• Build and maintain complex, multi-step LLM workflows, including with orchestration frameworks such as LangChain or LlamaIndex, for advanced reasoning, context reuse, and retrieval.
• Design and manage production prompt systems with dynamic prompting, context injection, and conditional logic.
• Own the deployment and release of LLM experiments, prompt management, and Langfuse-based evaluation pipelines.
• Run A/B tests across models, analyse results, and present data-driven impact assessments of AI features and experiments.
• Monitor AI system metrics, quality signals, latency, and release health using Langfuse and other observability tools.
• Deep-debug complex LLM chains using Langfuse traces, identifying bottlenecks and optimising for cost, latency, and context-window usage, and build output-scoring systems to root-cause hallucinations and logic errors.
• Write clean, scalable, and maintainable TypeScript code across the Next.js and Node.js stack.
• Build reliable backend logic for AI systems, with strong error handling, request validation, fallback flows, and predictable behaviour in production, including reliable tool execution and AI-to-service integrations.
• Ensure high code quality through testing, code reviews, and clear engineering standards.
• Monitor, troubleshoot, and improve production performance, reliability, and system health.
• Drive maintainability and technical quality through solid architecture, refactoring, and disciplined release practices.

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