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Head of AI Enablement Engineering

📅 Jul 9
AI EnablementEngineeringProduction-quality AgentsAI Tooling

📜 Description

  • Own and drive AI enablement engineering across Deepgram — the strategy, the standards, and the hands-on building that make AI leverage real in every function.
  • Personally evaluate, prototype with, and make the calls on the AI tools, agents, models, and orchestration layers Deepgram adopts; avoid tool sprawl and make pragmatic build-vs-buy decisions.
  • Build the reference implementations: reusable agents and skills, MCP servers, paved-road workflows, prompt and pattern libraries, and the enablement hub where the best internally-built tools are surfaced and elevated.
  • Set and run the company-wide AI adoption strategy — the metrics, milestones, and reporting cadence leadership uses to track progress, framed around measurable productivity and quality, not activity.
  • Partner with Platform/Internal Tools, Security, and Data to define guardrails that are embedded into platforms rather than enforced through gates — safe-use patterns, access, and data handling that make adoption easier.

🛠️ Requirements

  • A strong engineering background with the hands-on ability to build production-quality agents, tools, and automations yourself.
  • Deep, current fluency with the modern AI tooling landscape — coding agents, LLM application patterns, prompting, retrieval, MCP/agent tooling, and orchestration.
  • A track record of driving technology adoption and changing how people work at scale, in environments that didn't start out asking for it.
  • The ability to operate across business and technical functions and influence without direct authority, including credibility with senior engineering leaders.
  • Strong product and platform instincts — you treat enablement as a product, with users, adoption, and a roadmap.
  • Excellent communication — you can demo, document, evangelize, and report outcomes to executives in plain language.
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Lyft

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🕒 yesterday

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