Note: The job is a remote job and is open to candidates in USA. Deepgram is the leading platform underpinning the emerging trillion-dollar Voice AI economy, providing real-time APIs for speech-to-text and text-to-speech. They are seeking a Head of AI Enablement Engineering to own the mission of making Deepgram one of the most AI-native companies in the world by driving AI enablement engineering and building impactful tools and workflows.
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
Skills
- 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
- 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
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