Head of AI Engineering & Enablement

com ) 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.