AI Engineering Lead, Product Analytics

Summary: Product Analytics is building self-service tools and operating AI agents   that influence product development ; agents that   monitor   product health, surface anomalies, analyze user behavior, and produce the   insights   product leaders rely on. As more analysts build, the work needs   someone to scale agentic solutions and own   the shared infrastructure underneath   it . We are seeking an AI Engineering   Lead   to own that layer across a team of   roughly 35   analysts supporting 60+ products. You will build the shared repositories, standards, context, and evaluation tooling our analysts depend on, and you will define what production means for the team's AI work. You will also build agents yourself, often expanding what others have prototyped into something the whole team can use. This is a hands-on role; you build, and you keep our builders moving faster.   This is an ongoing leadership role that evolves as the field does, reporting directly to the VP, Product Analytics. The role reaches across TR. You will advocate for the data and tooling the team needs, push to get the right sources into the data lake, work with engineering and TR's central Data and Analytics team, and connect with AI leaders in other product groups so our work compounds with theirs.   W ithin 12 months we expect agents owning whole analytics workstreams, and this role builds the foundation that gets us there.   About the Role: As AI Engineering Lead, Product Analytics, you will   be responsible for :   Own the Shared Infrastructure:   Build and   maintain   the shared assets our analysts build on: the team's Git repositories, reusable components, context and data-access standards, and a registry of what exists and who owns it. Take what individual builders make locally and generalize it so the whole team can use it. Build this as self-service so analysts move forward by using the tooling, not by waiting on you.   Build Agents:   Build production AI agents yourself,   frequently   by picking up a tool another analyst prototyped and extending it into something more capable and broadly useful. Stay close enough to the build to keep your judgment about the tooling sharp.   Own Evaluations and the Definition of Done:   Define what production means for the team's AI work and own the evaluation standard that holds it there. Build the tooling that lets analysts run evals   themselves, and   bring the team's evaluation practice up over time.   Close Pipeline Gaps:   Find the breaks between collecting the right data and shipping the self-service AI tooling product managers use to understand user behavior in our products. Diagnose where data, context, or infrastructure is missing, drive the work to close those gaps, and advocate to get the right sources into the data lake.   Set the Build Standards:   Own how the team creates and manages its build artifacts: repository conventions, context files, documentation that makes agents reliable. Keep these changes cheap and fast to make so the standards speed builders up. Propose, with conviction, which workstreams should move fully to AI first, and sequence them so early wins build credibility.   Make Builders Better:   Bring analysts along by teaching the infrastructure they use; the person who sets the eval standard and the repository conventions is the one who shows people how to work with them. Keep the upskilling tied to real deliverables and to tooling people already touch, so the practice sticks.   Governance and Compliance:   Navigate TR's AI governance landscape on the team's behalf. Help analysts build   to TR standards , support compliance where agents touch sensitive data and decisions, and keep governance workable so it does not block shipping.   Scale Adoption Across the Team:   Make the team's tools findable and usable by someone who has no direct relationship with whoever built them. Keep the registry current, manage how tools move from prototype to shared and depended-on, and catch drift before it reaches stakeholders.   Interface Outward:   Represent the team in TR-wide AI conversations, connect with AI leaders in other product groups, and keep the link to TR's AI transformation program active. Manage the cross-team dependencies the work runs on, including data lake access and platform infrastructure.   Keep Production Agents Healthy:   Establish   how the team watches its own agents once they run in production, so breakage and quality drift get caught early. Give every production tool a clear owner and a monitored definition of done.   About You: This role suits someone who builds and who has run programs that scale across a team. You are a strong engineer who works alongside other builders, shaping infrastructure with them so they trust it and use it, and you have driven enough change to know how adoption   actually happens . You are a fit for the role of AI Engineering Lead if your background includes:   Demonstrated personal investment in AI: you actively track developments, experiment with new tools, and build things on your own initiative. Depth of curiosity and momentum carry the weight here.   Roughly 2 + years of serious hands-on building with modern AI tooling, with work you can point to. You are fluent across AI assistants, coding in an AI development environment, and the patterns of agent design, fluent enough to build production-grade tools and the shared infrastructure other builders rely on. Experience taking someone else's prototype and generalizing it into reusable infrastructure is a strong signal, and self-directed projects count as much as anything done on the job.   A working   knowledge   of how to evaluate AI systems: defining success criteria, building evals, and using them to decide what is ready for production. You can set this standard for others and improve it over time.   Substantial experience building structure and programs that scale a capability across a whole team: