Who you are

- At least 2 years of experience delivering practical applied AI or machine learning solutions that improve user efficiency and reliability

- Hands-on experience building production LLM applications, including RAG, prompt engineering, and tool or function calling

- Practical experience building agent workflows with guardrails, evaluation, and reliability in mind

- Strong Python programming skills, with modular, well-tested, production-grade code

- Experience with AWS services (such as ECS, Bedrock, SageMaker, or Secrets Manager), including networking and security principles

- Experience deploying services with Docker, CI/CD pipelines, and infrastructure as code

- SQL and data integration experience, including working with cloud data warehouses and enterprise APIs
- A working understanding of the machine learning lifecycle (feature engineering, training, evaluation, monitoring), sufficient to collaborate with data scientists and ML engineers

- Experience leading or supporting engineers through code and design reviews

- Transparent technical communication, with a bias toward information sharing and diverse viewpoints
- A creative, resourceful, and effective problem-solving approach

- Hands-on experience with agentic frameworks such as LangGraph, Strands, LangChain, or CrewAI

- Experience designing multi-agent systems, including orchestrator and sub-agent patterns, planning, tool use, and memory

- Experience integrating with enterprise sales or GTM platforms (such as Salesforce, Gong, or Conga), or familiarity with sales concepts like opportunity management, pipeline analytics, or MEDDPICC

- Experience with MCP servers and gateways for enterprise tool integration

- Knowledge of vector stores, embeddings, and retrieval pipelines

- Familiarity with responsible AI practices, including hallucination mitigation, privacy,



governance, and audit logging

- Experience with the Python ML stack (e.g., scikit-learn, PyTorch, Pandas, XGBoost/LightGBM) and data validation frameworks (e.g., Pandera, Excellent Expectations, or Pydantic)

- Effective use of AI coding assistants (e.g., Claude Code, Cursor, Copilot) in a team setting

What the job involves

- Thomson Reuters is seeking a Senior AI Engineer, Agentic AI to build and ship LLM-powered agents for our go-to-market organization. Our agent platform brings the tools sales teams use today into one AI-agent hub. You will build specialist agents that are scalable, reliable, and secure, and help set the engineering patterns the team relies on as it grows

- Design, develop, and deploy production LLM agents end to end, including multi-step workflows, retrieval-augmented generation (RAG), prompt design, tool and function calling, and structured-output validation

- Build specialist agents that ingest data from enterprise systems, extract actionable signals, and recommend next steps through a human-in-the-loop review

- Build and operate evaluation and observability that make agents trustworthy: guardrails, automated evals, drift detection, and production monitoring

- Integrate agents with AWS infrastructure and enterprise APIs, including MCP servers where approved

- Contribute to shared agent patterns so new agents reuse common context, guardrail, and evaluation components rather than rebuilding them

- Craft and sustain design documentation, architecture decision records, and system models

- Collaborate with platform, data integration, and governance engineers, plus product stakeholders, to turn business requirements into robust designs

- Provide technical guidance and code and design reviews, and mentor engineers as the team builds its agentic capability

The application process

- End Date: June 30, 2027

📌 Senior AI Engineer (Toronto)
🏢 Thomson Reuters
📍 Toronto