AI Engineering Leader: Scalable Agent Platform

Thomson Reuters; Toronto; On-site; CAD 140,000 - 175,000; Full time

Benefits offered by this job

Hybrid Work Model

Grow My Way training

Mental Health Days

Job summary

Refinitiv in Canada seeks an AI Engineering Lead for Product Analytics to own shared infrastructure, build production AI agents, and define the team’s standard of 'done'. You will scale tooling for ~35 analysts across 60+ products, building reusable components and governing data access.

This hands‑on leadership role emphasizes delivering practical tooling, mentoring builders, and partnering with data, security, and central analytics teams to accelerate AI initiatives.

Qualifications

• 2+ years hands‑on building with modern AI tooling.
• 4+ years driving change across teams and with senior stakeholders.
• 5+ years in analytics with a working command of a modern data stack.
• Awareness of AI governance and compliance considerations.

Responsibilities

• Own the shared infrastructure: build and maintain repositories, components, and data‑access standards.
• Build production AI agents and extend prototypes into reusable tools.
• Define production criteria and own evaluation standards for AI work.
• Close data and pipeline gaps to support self‑service tooling.
• Set build standards and repository conventions to accelerate adoption.
• Scale adoption across the team and manage cross‑team dependencies.
• Interface with TR’s AI leadership and ensure governance alignment.
• Keep production agents healthy with monitoring and ownerships.

Skills

AI tooling

Infrastructure scaling

Change management

Analytics leadership

Program coordination

Tools

Snowflake

SQL

Python

Power BI

Tableau

Streamlit

Job description

Refinitiv in Canada seeks an AI Engineering Lead for Product Analytics to own shared infrastructure, build production AI agents, and define the team’s standard of 'done'. You will scale tooling for ~35 analysts across 60+ products, building reusable components and governing data access.

This hands‑on leadership role emphasizes delivering practical tooling, mentoring builders, and partnering with data, security, and central analytics teams to accelerate AI initiatives.