[Hiring] Head of AI Enablement Engineering @Deepgram

Role Description

Deepgram's ambition is to build a generational company with a small, exceptional team — which only works if every engineer and every function operates with serious AI leverage. We're looking for a Head of AI Enablement Engineering to own that mission end to end: making Deepgram one of the most AI-native companies in the world, in practice and not just in principle.

This is a build-first leadership role, not a steward or training role. You'll personally evaluate tools, build the agents and workflows that show what great looks like, and set the standards that the rest of the company adopts. You'll turn our AI-native strategy into shipped capability — reusable agents and skills, MCP integrations, paved-road workflows, and the enablement hub and patterns that let any team go from idea to working tool fast and safely. You'll partner closely with Engineering, Platform/Internal Tools, People Ops, and functional leaders across the company, and you'll be measured on real outcomes: adoption, productivity, and the quality of what people build.

You'll lead largely through building and influence, with the runway to grow a small team and a network of champions as the function scales. It's a high-visibility seat with executive sponsorship and a mandate to set direction where there is no established playbook.

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.

Qualifications

• 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.

Requirements

• 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.