Head of AI Engineering & Enablement
Tebra only initiates contact with candidates via email from an official Tebra email address (@, @, or @) or through our applicant tracking system, Greenhouse. We will only ask you to provide sensitive personal information through our official application portal — not via social media or text message. We do not conduct interviews via instant messaging.
About the Role
We are hiring a hands-on player coach to lead AI across how Tebra runs as a company. You will be building alongside a small team of one to two engineers while simultaneously leading process re-engineering engagements with functional leaders. You will ship code, design agents and re-engineer workflows, while also leading the team around you.
With a small team, this role will focus on our internal operations — not the AI in our product. It covers how every function works, how fast we move, and how much leverage each person has. As we scale toward $300M+ in ARR, the goal is to decouple growth from headcount and build an operation that runs leaner as it gets bigger.
Most of the value comes from re-engineering the work itself, so you will pair deep engineering and applied AI skill with strong business judgment and a relentless focus on outcomes.
Your Area of Focus
AI Strategy & Use Case Discovery
- Work with the CEO, CFO, and CPO to identify where AI can drive the greatest efficiency and operating leverage across the organization, and prioritize accordingly.
- Audit and re-engineer business processes before automating them, so we improve how the work is done and not just how fast it runs.
- Build and maintain an AI Opportunity roadmap that prioritizes use cases by ROI, feasibility and strategic impact in partnership with the functional leaders.
Build the Hardest Workflows
- Perform deep-dive assessments to identify the highest-impact efficiency opportunities across all operating functions — then build them, don't just document them.
- Design and build high-value internal agents and automations that address the hardest problems inside our operating functions. Stay hands-on in the build yourself; this is not a role where you commission others and review outputs.
- Own the shared patterns for retrieval, agent design, and secure system-of-record connectivity — including MCP servers, agent-to-agent orchestration, and API integrations — with permission-aware access across Gong, Salesforce, NetSuite, Snowflake, Slack, and Workato.
- Design multi-agent systems where specialized agents hand off to each other across workflow steps, not just single agent automation.
- Build and maintain the organizational context layer, the connective tissue that makes Tebra queryable; meeting capture, knowledge connectors, MCP servers into our core systems and permission aware retrieval so agents and people have a single source of truth.
- Develop and maintain a library of reusable skills, frameworks, and how to guide, allowing one person’s breakthrough workflow scale to the entire organization and the programs compound over time.
- Own the full lifecycle from rapid prototyping to production-grade deployment, including monitoring, evaluation frameworks, error handling, and iteration based on real usage data.
Governance
- Define the approved tools, data-handling rules, build standards, and a shared reference architecture for AI across Tebra 's operating functions, in partnership with Legal and Security.
- Own how agents are deployed and monitored once live, ensuring full HIPAA compliance and strict adherence to our data privacy and security policies for PHI, without slowing teams down.
- Stand up an AI risk register, acceptable use policy, and audit trail standards for all production agents, and maintain them as the tooling landscape evolves.
Enable the Functions
- Partner with each function to find high-value use cases and help them build and ship the more routine, accessible agents themselves.
- Coach AI owners inside each function, and run enablement and fluency progr