Objective
Make AI adoption across the group's eight engineering organizations continuous and rhythmic: cascade each CTO's vision into their teams as working discipline, and move practices that already work in one company into the other seven.
About the project
Neurons Lab runs a group-wide AI Adoption Program for a major iGaming client: a holding of six game studios plus central business functions, 10+ companies, ~800–1,000 employees. The program combines business-team enablement, engineering enablement, and custom AI for game production.
This role owns the engineering enablement track exclusively. It is a new role, additional to the squad's AI Architect on the game-dev track; it does not build game-production pilots.
KPIs
- Diffusion: ≥2 practices packaged per month; ≥3 cross-company transfers per month; ≤2 weeks from detection to group-wide availability.
- Adoption: ≥1 experiment per active team per sprint; weekly-active AI usage ≥80% of engineers per active company.
- Outcomes: Maintain or improve PR throughput and lead-time trends (DX Core 4 / DORA) while ensuring change failure rates do not rise.
- Rhythm: Bi-weekly validation calls and monthly cross-company demo meets; maintain live status boards.
Areas of responsibility
- Turn CTO vision into team-level discipline: specs, rules, review standards, and reusable skills.
- Operate the diffusion loop: detect, validate, package, and transfer successful engineering practices across the group.
- Facilitate technical rhythm through validation calls, demo meets, and status tracking.
- Coach teams on defining objective, numeric success criteria for agentic work and loop engineering.
- Collaborate with local CTOs and triage complex needs to specialized internal teams.
- Manage group-level AI gateway agendas, cost attribution, and cloud infrastructure optimization.
Skills
- Hands-on daily fluency with agentic coding stacks: Claude Code, Cursor, Codex (including MCP servers, skills, and sub-agents).
- Expertise in AI architecture: LLM gateways/proxies (e.g., LiteLLM, OpenRouter), and in-region deployment patterns (e.g., Bedrock).
- Engineering-leadership credibility to review real code, pipelines, and specs with senior staff.
- Ability to package working practices into reusable artifacts (playbooks, templates, skills repos).
- Understanding of engineering measurement in the AI era, including DORA metrics and quality guardrails.