AI Enablement Lead

Technical Skills 5+ years of hands-on Software Engineering experience: Your specific tech stack (JS/TS, Python, PHP, Go, Java) matters less than genuine engineering fluency. You must be able to speak the "developer's language" credibly.Scripting & Prototyping: Working competence in at least one language like Go, JavaScript/TypeScript, or PHP to build integrations, glue code, and prototypes.Practical AI/LLM Experience: 1+ year of deep, hands-on experience with LLMs (Claude, GPT, etc.) beyond casual use. You understand prompt design, agentic workflows, and the real-world strengths/limits of current models. (Recent depth counts more than tenure).AI Workflows: Proven experience configuring or building AI-assisted developer tools (e.g., AI coding assistants, RAG architectures, agents).Integration & Systems: Strong API integration skills (RESTful services, auth, webhooks) and solid command of standard Git workflows.Security Awareness: Sound understanding of data security, privacy, and access-control basics when handling sensitive company data.Soft Skills & MindsetExceptional Communication: You can explain complex technical concepts to both engineers and non-technical teams. You write playbooks, docs, and training materials that people actually use. (Note: This is a communication-first role; technical brilliance cannot offset a lack of communication skills).Enablement & Mentoring: Proven experience in mentoring, technical onboarding, running internal talks, or building communities of practice.Influence Without Authority: You know how to build enthusiasm, drive adoption, and shift behaviors across teams that do not report to you.Public Speaking: Comfortable facilitating workshops and presenting to groups (or possessing a clear aptitude and willingness to develop this skill quickly).Self-Directed Problem Solver: You are proactive, highly analytical, and comfortable defining your own roadmap in an emerging, ambiguous space.Language Proficiency: English at a B2 level is the absolute minimum, but C1 is strongly preferred due to the heavy focus on company-wide written and spoken communication. Nice to have:Specific AI Stack: Hands-on experience with Claude Cowork and MCP (Model Context Protocol) is highly preferred (though a strong engineer can learn this quickly).DevRel Background: Prior experience in Developer Advocacy, Developer Relations, or formal internal technical enablement.Technical Writing: Experience creating formal technical documentation, courseware, or developer-facing content.Frontend Basics: Familiarity with a modern frontend framework (React/Next.js) for building internal tools, dashboards, and demos.Data & RAG Context: Comfort with SQL and exposure to NoSQL or vector databases.Infrastructure: Working knowledge of Docker, CI/CD concepts, and containerized environments (Kubernetes is a plus). We are looking for a unique blend of a hands-on software engineer and a passionate technical educator to drive our internal AI transformation. As a key member of our team, you will be responsible for turning generic AI capabilities into company-specific leverage by setting up reliable tooling, defining security guardrails, and creating comprehensive learning paths. You will act as the central bridge between engineering and the broader business, championing AI adoption and shifting workflows across various 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 a direct impact on the productivity and culture of our entire organization. ,[Own the AI Tooling Setup: Hands-on installation, configuration, and integration of AI tools (e.g., Claude, Copilot, Cursor) with internal systems via MCP servers to build context-aware, reliable AI workflows., Drive Phased AI Adoption: Lead the rollout of AI tools, starting with engineering teams (complex workflows, agents) and systematically expanding to non-technical departments., Design Enablement Programs: Create and evolve comprehensive training curriculums, playbooks, workshops, and self-serve documentation to upskill the entire company on LLMs., Define 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., Empower an "AI Champion" Network: Identify, train, and support a community of local AI advocates within various teams who will help multiply adoption and share knowledge horizontally., Ensure Practical 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., Evaluate AI Tools Objectively: Maintain a defensible, evidence-based methodology for selecting AI tools, benchmarking them against real internal workflows rather than vendor hype., Measure and Report Impact: Track AI adoption metrics, replacing anecdotes with hard data to show where AI creates real leverage and where we should invest next., Maintain 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., Scale Through Self-Serve Resources: Focus on scaling your impact through documentation, courses, and structural improvements rather than 1:1 tutoring., Travel: Occasionally visit company offices to run in-person workshops, onboard teams, and collaborate directly with engineers and stakeholders.] Requirements: Communication skills, AI, Engineering, Scripting language, LLM, API, Mentoring, MCP, Claude Cowork, Technical writing, Frontend, Data, CI/CD Tools: . Additionally: Sport subscription, Private healthcare, Small teams, Free coffee, Bike parking, Playroom, Free parking, Modern office, No dress code.