AI Enablement Lead

Travelplanet.pl SA (Invia Group) is hiring for a role focused on driving internal AI transformation by setting up AI tooling, defining security guardrails, and creating learning paths to enable company-wide AI adoption.

• 5+ years of hands-on Software Engineering experience with fluency in developer languages (JS/TS, Python, PHP, Go, Java).
• Working competence in scripting/prototyping languages like Go, JavaScript/TypeScript, or PHP.
• 1+ year of deep, hands-on experience with LLMs (Claude, GPT, etc.) including prompt design and agentic workflows.
• Proven experience configuring or building AI-assisted developer tools (e.g., AI coding assistants, RAG architectures, agents).
• Strong API integration skills (RESTful services, auth, webhooks) and solid command of Git workflows.
• Sound understanding of data security, privacy, and access-control basics.
• Exceptional communication skills to explain complex technical concepts and write usable playbooks, docs, and training materials.
• Proven experience in mentoring, technical onboarding, internal talks, or building communities of practice.
• Ability to influence without authority and drive adoption across teams.
• Comfortable facilitating workshops and public speaking or willingness to develop this skill.
• Proactive, analytical, and self-directed problem solver.
• English language proficiency at minimum B2 level, C1 preferred.

Nice to have:

• Hands-on experience with Claude Cowork and MCP (Model Context Protocol).
• Background in Developer Advocacy or Developer Relations.
• Experience in technical writing and creating developer-facing content.
• Familiarity with modern frontend frameworks (React/Next.js).
• Comfort with SQL and exposure to NoSQL or vector databases.
• Working knowledge of Docker, CI/CD concepts, and containerized environments (Kubernetes is a plus).

• 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 and expanding to non-technical departments.
• Design Enablement Programs: Create and evolve training curriculums, playbooks, workshops, and documentation to upskill the company on LLMs.
• Define AI Standards & Best Practices: Codify prompting guidelines, build AI-ready documentation, and establish reusable workflow patterns.
• Empower an "AI Champion" Network: Identify, train, and support a community of local AI advocates within teams.
• Ensure Practical Security Guardrails: Partner with Security, IT, and Legal to implement data privacy, prompt hygiene, and IP risk defaults.
• Evaluate AI Tools Objectively: Maintain evidence-based methodology for selecting AI tools and benchmarking.
• Measure and Report Impact: Track AI adoption metrics to identify leverage points and investment areas.
• Maintain Hands-on Credibility: Prototype integrations, dogfood tools daily, and build reference workflows.
• Scale Through Self-Serve Resources: Focus on documentation, courses, and structural improvements rather than 1:1 tutoring.
• Travel occasionally to company offices for workshops and collaboration.