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.

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.

It's Important To Us That You Have
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.

Skills Required
Strong engineering background with hands-on ability to build production-quality agents, tools, and automations.
Deep, current fluency with modern AI tooling: coding agents, LLM application patterns, prompting, retrieval, MCP/agent tooling, and orchestration.
Proven track record driving technology adoption and changing how people work at scale.
Ability to operate across business and technical functions and influence without direct authority; credibility with senior engineering leaders.
Strong product and platform instincts; treat enablement as a product with users, adoption metrics, and a roadmap.
Excellent communication: 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.
Hands-on builder: personally evaluate tools, prototype, and make build-vs-buy decisions.
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.
Experience in a fast-moving, AI-native engineering organization.