Head Of AI Enablement Engineering (AI)

TL;DR

Head of AI Enablement Engineering (AI): Driving the transition to an AI-native company by building reusable agents, workflows, and MCP integrations with an accent on operationalizing AI leverage across all functions. Focus on creating reference implementations, setting company-wide adoption standards, and designing safe-use guardrails for AI tools.

Location: Remote (USA)

Company

Deepgram is a leading platform for Voice AI, providing real-time APIs for speech-to-text, text-to-speech, and production-grade voice agents.

What you will do

• Drive AI enablement strategy and standards, building hands-on tools to increase AI leverage across every function.
• Evaluate and prototype AI tools, models, and orchestration layers to avoid tool sprawl and make build-vs-buy decisions.
• Create reference implementations, including reusable agents, MCP servers, and prompt and pattern libraries.
• Define and track company-wide AI adoption metrics focused on measurable productivity and quality.
• Partner with Security and Platform teams to embed safe-use guardrails and data-handling practices into internal systems.
• Build a distributed network of AI champions and scale a central enablement team over time.

Requirements

• Must be based in the USA.
• Strong engineering background with the ability to build production-quality agents and automations.
• Deep fluency in modern AI tooling: LLM application patterns, prompting, retrieval, and MCP/agent orchestration.
• Proven track record of driving large-scale technology adoption and changing how people work.
• Strong product instincts, treating internal enablement as a product with users, adoption metrics, and a roadmap.
• Ability to influence senior engineering leadership and communicate outcomes to executives in plain language.

Nice to have

• Experience starting an AI enablement, developer productivity, or engineering effectiveness function from scratch.
• Background in building internal platforms or developer-facing tooling that saw high adoption.
• Familiarity with enterprise AI search and knowledge tooling like Glean or Notion AI.
• Experience in a fast-moving, AI-native engineering organization.

Culture & Benefits

• AI-first culture where experimentation and the use of advanced AI tools are core to performance.
• High-visibility role with executive sponsorship and a mandate to set direction where no playbook exists.
• Fast-paced environment emphasizing rapid evolution, agility, and continuous learning.
• Philosophy of utilizing small, AI-leveraged teams to outbuild much larger organizations.