deepgram - Head of AI Enablement Engineering
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. • Comfort defining safe-use guardrails and data-handling practices in partnership with Security and Platform. • IT WOULD BE GREAT IF YOU HAD • Experience standing up an AI enablement, developer productivity, or engineering effectiveness function from scratch. • Background building internal platforms or developer-facing tooling that engineers actually adopted. • Experience leading a small team and/or a distributed champions/center-of-excellence model. • Familiarity with enterprise AI search and knowledge tooling (e.g., Glean, Notion AI) and agent orchestration frameworks. • A point of view on measuring developer productivity and AI impact, with the nuance that entails. • Experience in a fast-moving, AI-native engineering organization.
Responsibilities
• 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, not harder. • Build and lead a distributed champions network embedded in teams, and grow a small central team over time as impact scales. • Partner with People Ops on AI-native onboarding and fluency, so new and existing teammates do real reps inside the tools and leave the system better than they found it. • Stay ahead of a fast-moving landscape and translate emerging AI capabilities into pragmatic, Deepgram-ready practice. • YOU'LL LOVE THIS ROLE IF YOU • Want to define how an entire company works with AI — and you'd rather build the proof than write the memo. • Are energized by ambiguity and a blank page, and you set direction where there's no playbook yet. • Are hands-on and current: you build agents and workflows yourself and can sit across from senior engineers as a peer on day one. • Care about real outcomes — adoption, time saved, quality — not vanity metrics or shelf-ware. • Like operating across an org, bringing skeptical teams along through demonstrated value rather than mandate. • Believe a small, AI-leveraged team can outbuild a much larger one.
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