Teams at Thomson Reuters

Leads the design, development, evaluation, and production deployment of document understanding systems for legal, tax, and accounting content. Responsibilities include semantic chunking, document enrichment, hierarchical classification, entity and citation extraction, knowledge graph construction, LLM post-training, model distillation, synthetic data generation, and evaluation framework development. The role guides architecture and AI strategy, partners with engineering on scalable delivery, influences leadership, and mentors applied scientists and ML practitioners.

Architects and ships production AI agent systems, including multi-component pipelines, tool integrations, orchestration infrastructure, observability, evaluations, and reliability practices. The role owns complex systems end-to-end, contributes to technical architecture and progressive delivery, collaborates across product and engineering, and mentors engineers. It requires strong backend, distributed systems, cloud infrastructure, LLM, and agentic development experience.

Build, train, evaluate, and deploy production machine learning and LLM systems for legal document intelligence. Develop NLP solutions for information extraction, summarization, search, AI agents, and document analysis. Create scalable ML pipelines, evaluation frameworks, and reliability metrics while optimizing model performance. Collaborate with engineering, product, legal, data, and security teams to deliver secure AI capabilities and translate business problems into production solutions.