Technical AI Engagement Lead

Job Description

Areas of responsibility

• 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

• 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

• 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

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