AI Engineer, Product Analytics
Thomson Reuters6 days ago
Toronto, CanadaMid Level
H1B Sponsor
Responsibilities
- Integrate pre-trained models, LLM APIs, tool-calling, and MCP-style integrations into analytics products.
- Refactor experimental notebooks and prototypes into production-ready components with error handling, guardrails, telemetry, and logging.
- Prepare, clean, structure, model, and pipeline the data used by AI agents.
- Work within shared repositories, reusable components, context standards, and data-access standards.
- Evaluate AI systems using validation sampling, source traceability, prompt and model version documentation, and testing for non-deterministic behavior.
- Monitor agent health and quality in production, conduct root cause analysis, and update context, prompts, and logic.
- Contribute to proactive intelligence systems, natural-language interfaces, semantic layers, and feedback loops.
- Translate ambiguous business needs into agent designs and collaborate with product and engineering teams.
- Participate in planning, code reviews, team ceremonies, documentation, and status reporting.
Requirements
- Bachelor's degree in Computer Science, Data Science, or a related field.
- At least 3 years of experience in software engineering, data engineering, data science, or analytics.
- Hands-on experience building or contributing to LLM-based agents or AI-native systems.
- Demonstrated initiative in experimenting with AI tools and building relevant projects.
- Proficiency in Python and strong SQL, with grounding in programming concepts, design patterns, SDLC principles, and unit testing.
- Exposure to RAG pipelines, multi-step reasoning agents, tool-calling, MCP-style integrations, vector databases, embeddings, semantic search, and retrieval pipelines.
- Experience refactoring experimental or research code into production-ready components.
- Familiarity with MLOps, LLMOps, AI software lifecycles, testing, evaluation, and monitoring.
- Clear written and verbal communication skills, including explaining technical concepts to non-technical stakeholders.
- Preferred qualifications include product-team or SaaS experience, experiment design, statistical inference, A/B testing, applied machine learning, LLM API integration, advanced retrieval, agent orchestration, context engineering, guardrails, observability, modern data stacks, cloud computing, containerization, and AI governance.
Benefits
- Flexible hybrid work model for office-based roles.
- Work-from-anywhere flexibility for up to 8 weeks per year.
- Flexible workplace policies supporting personal and professional responsibilities.
- Career development, continuous learning, and skills-first growth programs.
- Comprehensive benefits including flexible vacation, two company-wide Mental Health Days, Headspace access, retirement savings, tuition reimbursement, incentive programs, and wellbeing resources.
- Two paid volunteer days annually and opportunities for pro-bono consulting and ESG initiatives.
Categories
AI ApplicationsData Engineering
About Thomson Reuters
Thomson Reuters (TSX/NDAQ: TRI) informs the way forward by bringing together the trusted content and technology that people and organizations need to make the right decisions. We serve professionals across legal, tax, accounting, compliance, government, and media. Our products combine highly specialized software and insights to empower professionals with the data, intelligence, and solutions needed to make informed decisions, and to help institutions in their pursuit of justice, truth, and transparency. Reuters, part of Thomson Reuters, is a world leading provider of trusted journalism and news. For more information on Thomson Reuters, visit tr.com and for the latest world news, reuters.com.