ph3Objective /h3 pMake 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. /p h3Objective /h3 pMake 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. /p h3About The Project /h3 pNeurons 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. /p pThis role owns the bengineering enablement track exclusively /b — the direct counterpart of the AI Education/Engagement Manager, who owns business teams. It is a bnew role, additional to the squad's AI Architect on the game-dev track /b; it does not build game-production pilots. /p pThe 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 bpractices, not tools /b. /p pbDuration: /b ongoing, client-dedicated. bStage: /b start. /p h3KPIs /h3 ul liDiffusion (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 /li liAdoption: ≥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) /li liOutcomes:



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 /li liRhythm: 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 /li /ul h3Areas of responsibility /h3 ul liInside 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 /li liRun 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 /li liOperate 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 /li liTeach 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 /li liRespect 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 /li liTriage 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 /li liFeed 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 /li liCapture everything reusable in a group knowledge base; make wins visible to the CTOs and group leadership /li /ul h3Skills /h3 ul liHands‑on daily fluency with agentic coding stacks: Claude Code, Cursor, Codex — including MCP servers, skills, sub‑agents, and spec‑driven development on real repositories /li liAI architecture: LLM gateways/proxies (LiteLLM / OpenRouter class), cost and error attribution, local-LLM trade‑offs, in‑region deployment patterns (e.g. Bedrock) /li liEngineering‑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 /li liFacilitation of technical sessions: live demos, validation calls, hands‑on workshops with real repos /li liPackaging: turning a working practice into an artifact another team adopts without the author in the room /li /ul h3Knowledge /h3 ul liEngineering measurement in the AI era: DX Core 4 / DORA, AI‑impact metrics, quality guardrails for AI‑generated code /li liAdoption psychology for senior engineers — resistance among seniors is a named blocker in several of the client's companies /li liGame development / iGaming exposure (nice to have): game math, certification constraints, art/animation pipelines /li /ul h3Experience /h3 ul liLed AI adoption or platform/developer‑enablement work in an engineering organization (20+ engineers), or equivalent tech‑lead/head‑of‑engineering experience /li liShipped agentic workflows to production; can show their own skills, MCP servers or spec repositories /li liFluent English required; Russian and/or Ukrainian a strong plus — the client's teams communicate in both /li /ul /p #J-18808-Ljbffr

📌 Technical AI Engagement Lead (España)
🏢 Neurons Lab
📍 España