Engineering AI Adoption Lead

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.

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 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