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Strong brand and metro location increase applicants, but required TR internship narrows the candidate pool.
Strong preference for TR internship and LLM-specific stack reduces cross-industry transferability.
Mandatory TR internship plus specific LLM, LangChain, and AWS Bedrock experience makes filters highly strict.
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Implement and own LangChain and LangGraph components including chains, memory, tools, and state graphs with growing independence.
Build, evaluate, and optimize RAG pipelines including chunking, embedding, retrieval, and accuracy testing.
Contribute to proof of concept (POC) delivery and write clean, testable Python code participating actively in code reviews.
Completed a 6-month internship with the Automation and AI CoE with demonstrated contribution to live projects.
Proficient in Python with production-quality coding skills beyond AI-generated code.
Hands-on experience with LangChain or LangGraph including state management and tooling.
Experience working with OpenAI models, Claude, Gemini, or Llama including meaningful integration (not just API calls).
Has demonstrated ability to build AI components and RAG pipelines during an Automation and AI Center of Excellence internship.
Experienced in working within the Thomson Reuters technology stack, including AWS Bedrock / AgentCore and low code automation tools like Microsoft Copilot Studio and Power Automate.
Comfortable using AI coding tools daily (Claude Code, GitHub Copilot, Cursor, Cline) with strong judgment on their effective use and understands Git collaborative workflows.