AI Engineering Lead, Product Analytics

Thomson Reuters•Toronto, ON

2d•CA$140,000 - CA$175,000•Hybrid

About The Position

Product Analytics is building self-service tools and operating AI agents that influence product development. These agents monitor product health, surface anomalies, analyze user behavior, and produce insights for product leaders. As more analysts build, there is a need for someone to scale agentic solutions and own the shared infrastructure. This role will own that layer across a team of roughly 35 analysts supporting over 60 products. The AI Engineering Lead will build the shared repositories, standards, context, and evaluation tooling that analysts depend on, and will define what production means for the team's AI work. This is a hands-on role where the lead will also build agents, often expanding prototypes into team-wide solutions. This is an ongoing leadership role that evolves with the field, reporting directly to the VP, Product Analytics. The role spans across Thomson Reuters (TR), advocating for necessary data and tooling, working to integrate the right sources into the data lake, collaborating with engineering and TR's central Data and Analytics team, and connecting with AI leaders in other product groups to ensure work compounds effectively. Within 12 months, the expectation is for agents to own entire analytics workstreams, and this role is foundational to achieving that goal.

Requirements

Demonstrated personal investment in AI, including tracking developments, experimenting with new tools, and building independently.
Approximately 2+ years of hands-on experience with modern AI tooling, with demonstrable projects.
Fluency in AI assistants, AI development environments, and agent design patterns, sufficient to build production-grade tools and shared infrastructure.
Experience generalizing prototypes into reusable infrastructure; self-directed projects are valued.
Working knowledge of AI system evaluation, including defining success criteria, building evaluations, and determining production readiness.
Substantial experience building structure and programs that scale capabilities across a team (standards, processes, cadences, documentation).
Strong attention to detail and discipline in specifying and verifying success criteria.
Judgment to guide the team on AI adoption and articulate vision to analysts and product leaders.
4+ years of experience driving change across teams and with senior stakeholders, with a proven track record of influencing adoption of new working methods.
5+ years in analytics or a closely related data discipline, with proficiency in a modern stack (e.g., Snowflake, SQL, Python, BI tools like Power BI or Tableau, Streamlit).
Awareness of AI governance and compliance considerations.

Nice To Haves

Project tracking and program coordination experience (e.g., Linear, Azure DevOps, SharePoint).
Experience in or alongside product teams.
SaaS experience.

Responsibilities

Own the Shared Infrastructure: Build and maintain shared assets including Git repositories, reusable components, context and data-access standards, and a registry of existing tools and their owners. Generalize individual work into team-usable solutions, making it self-service.
Build Agents: Develop production AI agents, often by extending prototypes into more capable and broadly useful tools. Maintain hands-on involvement to ensure tooling quality.
Own Evaluations and the Definition of Done: Define production standards for AI work and own the evaluation criteria. Build tooling for analysts to run evaluations independently and improve the team's evaluation practices.
Close Pipeline Gaps: Identify and address issues in the data collection to self-service AI tooling pipeline. Diagnose missing data, context, or infrastructure, drive improvements, and advocate for data integration into the data lake.
Set the Build Standards: Establish and manage team standards for creating and managing build artifacts, including repository conventions, context files, and documentation for agent reliability. Ensure these standards facilitate rapid and cost-effective changes.
Make Builders Better: Mentor analysts by teaching the infrastructure they use, including evaluation standards and repository conventions. Align upskilling with real deliverables and existing tooling.
Governance and Compliance: Navigate TR's AI governance framework, ensure agents comply with TR standards, support compliance with sensitive data and decisions, and maintain workable governance that doesn't impede shipping.
Scale Adoption Across the Team: Make team tools discoverable and usable by all analysts. Maintain the tool registry, manage the transition of tools from prototype to shared assets, and prevent drift that impacts stakeholders.
Interface Outward: Represent the team in TR-wide AI discussions, connect with AI leaders in other product groups, and maintain engagement with TR's AI transformation program. Manage cross-team dependencies, including data lake access and platform infrastructure.