Head of AI Engineering & Enablement

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 compliance and performance.