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.
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.
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 pulse
Inside each company: take the cascade load off the CTO — turn their vision into team-level discipline: specs, rules, review standards, reusable skills, onboarding of the next circle of engineers
Run the diffusion loop between companies: detect what already works in one team, validate direction and risks, package it into a reusable artifact, transfer it to the rest, measure against objective criteria
Operate the rhythm: bi-weekly validation calls with active teams (an empty sprint is a signal to reorganize, not to push harder), a monthly cross-company demo meet, a per-company status board, and a monthly steering sync with the group CTO
Teach teams to define objective, numeric success criteria for agentic work (loop engineering / hill-climbing against a metric) — the single biggest success factor for agents in production
Respect each company's protocols: work through the local CTO first (some CTOs require being the first point of contact for all technical topics), never around them
Triage needs that exceed enablement into scoped units — workshops (with the Head of AI Engineering), PoCs, deep-dive reviews — and hand them to the right Neurons Lab team
Feed the group-level gateway/attribution agenda: cost and error attribution per team and tool; collaborate with the cloud team on cost optimization and AWS credits/co-funding
Capture everything reusable in a group knowledge base; make wins visible to the CTOs and group leadership
Hands-on daily fluency with agentic coding stacks: Claude Code, Cursor, Codex — including MCP servers, skills, sub-agents, and spec-driven development on real repositories
AI architecture: LLM gateways/proxies (LiteLLM / OpenRouter class), cost and error attribution, local-LLM trade-offs, in-region deployment patterns (e.g. Bedrock)
Engineering-leadership credibility at tech-lead / AI-architect / head-of-engineering level — able to review real code, pipelines and specs with senior engineers, not present slides
Facilitation of technical sessions: live demos, validation calls, hands-on workshops with real repos
Packaging: turning a working practice into an artifact another team adopts without the author in the room
Engineering measurement in the AI era: DX Core 4 / DORA, AI-impact metrics, quality guardrails for AI-generated code
Adoption psychology for senior engineers — resistance among seniors is a named blocker in several of the client's companies
Game development / iGaming exposure (nice to have): game math, certification constraints, art/animation pipelines
Led AI adoption or platform/developer-enablement work in an engineering organization (20+ engineers), or equivalent tech-lead/head-of-engineering experience
Shipped agentic workflows to production; can show their own skills, MCP servers or spec repositories
Fluent English required; Russian and/or Ukrainian a strong plus — the client's teams communicate in both
