Technical AI Engagement Lead
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Reach the decision-maker — $5About the role
**Objective**
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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**
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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**
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* **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
**Areas of responsibility**
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* 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
**Skills**
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* 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
**Knowledge**
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* 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
**Experience**
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* 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
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