Technical AI Engagement Lead

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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 — the direct counterpart of the AI Education/Engagement Manager, who owns business teams. It is a new role, additional to the squad's AI Architect on the game-dev track; it does not build game-production pilots.

The engineering organizations span the full maturity range — from production agentic workflows, custom MCP servers and an AI-gateway rollout in the strongest companies, to teams writing their first specs.



Every company keeps its own tools (Cursor / Claude Code / Codex — diversity is deliberate policy); this role transfers practices, not tools.

Duration: ongoing, client-dedicated. Stage: start.

KPIs

Diffusion (core): ≥2 practices packaged per month into reusable artifacts (playbook, spec template, skills repo, recorded demo); ≥3 cross-company transfers per month, each adopted by ≥2 further companies; ≤2 weeks from detection to group-wide availability
Adoption: ≥1 experiment per active team per sprint ("no empty sprints"); weekly-active AI usage ≥80% of engineers per active company (targets calibrated after 30-day baseline)
Outcomes: developer time savings vs baseline; PR throughput and lead-time trend (DX Core 4 / DORA); guardrail — change failure rate and rework must not rise as AI share grows
Rhythm: bi-weekly validation calls and monthly cross-company demo meets held on cadence; live one-page status board per company; CTO satisfaction ≥8/10 on a quarterly

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