Head of AI Enablement Engineering

What You'll Do

• 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.

Requirements

• 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.
• Track record of driving technology adoption and changing how people work at scale, in environments that didn't start out asking for it.
• 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.

Nice-to-Haves

• 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.