SummaryProduct Analytics is building self‑service tools and operating AI agents that influence product development, monitor product health, surface anomalies, analyze user behavior, and produce the insights product leaders rely on. As the team grows, the role of an AI Engineering Lead is to scale these agentic solutions and own the shared infrastructure that underpins them across a team of roughly 35 analysts supporting 60+ products. The role is hands‑on, leading the development of shared repositories, standards, context, evaluation tooling, and defining production readiness for the team’s AI work. Reporting directly to the VP of Product Analytics, the lead will advocate for the data and tooling the team needs, ensure alignment with TR’s central Data and Analytics team, and coordinate with AI leaders in other product groups to complement their work.ResponsibilitiesOwn the Shared Infrastructure:Build and maintain the shared assets our analysts use—including Git repositories, reusable components, context and data‑access standards, and a registry of ownership. Generalize local builds into reusable resources, making them self‑service so analysts can progress without waiting for a lead.Build Agents:Build production AI agents, often taking a prototype developed by an analyst and extending it into a broader, production‑ready solution.Define Production and Evaluation:Own the definition of “production” for the team’s AI work and the evaluation standard that enforces it. Provide tooling that lets analysts run their own evaluations and improve the quality praxis over time.Close Pipeline Gaps:Identify missing data, context, or infrastructure that prevents shipping self‑service AI tooling to product managers. Diagnose gaps, drive remediation, and advocate for the required data sources in the lake.Set Build Standards:Own repository conventions, context file design, documentation,



and artifact management that keep agents reliable and deployments fast.Champion AI First Workstreams:Propose and sequence workstreams that should move to AI first, providing early wins to build credibility.Teach and Upskill Builders:Mentor analysts on infrastructure, evaluation standards, and repository conventions, tying training to real deliverables.Governance and Compliance:Navigate TR’s AI governance landscape to help analysts build within TR standards, supporting compliance for sensitive data and decision points.Scale Adoption:Manage the registry, ensure discoverability of tools, guide prototypes to shared artifacts, and keep dependencies current to prevent drift.Interface Outward:Represent the team in TR‑wide AI conversations, manage cross‑team dependencies (data lake, platform infrastructure), and maintain ties to TR’s AI transformation program.Keep Production Agents Healthy:Establish monitoring for production agents, assign clear ownership, and define when breakage or quality drift should be addressed.QualificationsDemonstrated personal investment in AI: actively tracks developments, experiments with new tools, and builds with initiative.2+ years of serious hands‑on building with modern AI tooling (prototypes, code, concepts).Fluent across AI assistants, AI development environments, and agent design patterns—able to deliver production‑grade tools.Experience taking a prototype and generalizing it into reusable infrastructure; self‑directed projects count.Working knowledge of AI system evaluation: define success metrics, build evaluation pipelines, and determine readiness for production.Substantial experience building programs that scale a capability across a team—standards, processes, cadences,



documentation.A strong eye for detail, discipline in specifying success, and continuous verification after building.4+ years of driving cross‑team change, coordinating with senior stakeholders, and achieving adoption.5+ years in analytics or a closely related field, proficient in a modern stack (e.G., Snowflake, SQL, Python, BI tools such as PowerBI or Tableau, Streamlit).Awareness of AI governance and compliance considerations.Project tracking and program coordination experience (e.G., Linear, Azure DevOps, SharePoint) is an asset.Experience in or alongside product teams and SaaS experience are additional assets.BenefitsHybrid work model—2–3 days a week in office, versatile work from anywhere up to 8 weeks per year.Flex My Way policies to support personal and professional responsibilities.Career development and growth programs with skill‑first initiatives.Competitive base pay range (e.G., Ontario: $140,000CAD–$175,000CAD) and eligibility for an annual bonus.Retirement savings, tuition reimbursement, and comprehensive benefits including mental health resources.Paid volunteer days and opportunities to contribute to social impact and ESG initiatives.CompensationThe base compensation range varies across locations. For Ontario, Canada, the base compensation range is $140,000CAD–$175,000CAD. Additional compensation may include an annual bonus based on enterprise and individual performance.Equal Employment OpportunityWe are an Equal Employment Opportunity Employer. To ensure a diverse workforce, we seek talented, qualified employees of all backgrounds regardless of race, color, sex/gender, sexual orientation, disability, age, marital status, or other protected classifications under applicable law. We provide reasonable accommodations for qualified individuals with disabilities and for sincerely held religious beliefs.This position is open due to an existing vacancy to support evolving business needs.#J-18808-Ljbffr

📌 Ai Engineering Lead, Product Analytics - C$140,000 - C$175,000 A Year (Winnipeg)
🏢 Refinitiv
📍 Winnipeg