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Thomson Reuters

Legal, financial data, and media services

Lead Software Engineer AI

AI Application Engineering +1Confirmed live in the last 24 hours
Full-timeMexico City, CDMX, MexicoNo salary listed

About the Job

Requirements

  • A bachelor's degree in Computer Science, Computer Engineering, a related field, or equivalent experience.
  • A proven track record of shipping production-grade AI systems to customers, including deployment, monitoring, evaluation, scaling, and ongoing operational ownership in enterprise environments.
  • At least 10 years of cumulative experience, including 2 years in a lead role and at least 5 years of full-stack development building scalable cloud-based applications, web services, APIs, and AI-driven products.
  • Experience with production systems using frameworks such as ASP.NET Core or similar, relational databases such as SQL Server or PostgreSQL or equivalent, and a major cloud provider; AWS is preferred and Azure is acceptable. C#/.NET expertise is a strong plus but not mandatory.
  • Experience architecting and implementing accessible front-end solutions using JavaScript, TypeScript, HTML, and CSS, and experience with modern JavaScript frameworks such as Angular or React.
  • A strong background in distributed systems, including data modeling, API contracts, observability, resilience patterns, and performance tuning under load.
  • Communication skills and ability to partner with product, design, and machine-learning teams in a fast-moving environment.
  • Demonstrated interest in AI systems and new engineering paradigms, including large language models, agents, and retrieval.
  • Prior experience in domains where correctness, auditability, and compliance matter, or strong interest in applying AI in those contexts.

Responsibilities

  • Define patterns for building MCP servers, designing agents, using large language models, experimentation, and scalable infrastructure.
  • Build and evolve systems operating over millions of documents, structured tax data, changing laws, and thousands of concurrent AI interactions.
  • Lead collaboration across engineering teams, product, user experience design, and AI Labs partners to research, design, and ship products.
  • Shape platform integration strategy across new and existing audit systems.
  • Lead multi-quarter initiatives across AI, product, and infrastructure, such as an orchestration layer, low-latency retrieval system, or unified knowledge base.
  • Mentor senior and mid-level engineers on system design, code quality, and AI integration practices.
  • Collaborate with AI and machine-learning engineers, researchers, designers, and product managers to translate new model capabilities into reliable user-facing workflows.
  • Help shape the team's roadmap, technical strategy, and engineering culture, including experimentation, testing, rollout, and postmortems.
  • Own the end-to-end architecture and design of backend services using C#/.NET, Python, FastAPI, PostgreSQL, AWS, and Vercel to integrate heterogeneous systems and power generative AI agents, complex workflows, and document-centric experiences.
  • Build and evolve AI orchestration, including routing, tool calling, MCP servers, multi-step workflows, safety and guardrails, and error handling around third-party large language models.
  • Design for high-throughput, low-latency AI workloads using caching, queuing, rate-limiting, model failover, and cost/performance trade-offs.
  • Work with large-scale data, including documents, retrieval and search, vector stores, and indexing strategies for tax and accounting use cases.
  • Establish and refine service-level objectives, observability, and incident response for AI systems used in professional workflows.

Desired Qualifications

  • Experience designing and implementing continuous integration and continuous delivery pipelines, such as GitHub Actions.
  • Hands-on experience integrating large language model APIs such as OpenAI and Anthropic into production applications, including prompt/response management, cost controls, and safety considerations.
  • Experience with AI-adjacent infrastructure such as vector databases, embeddings, semantic search, or custom retrieval pipelines.
  • Experience and informed opinions on automated testing, reliability, and release practices for systems with non-deterministic model behavior.

About the Company

Thomson Reuters

Legal, financial data, and media services

Thomson Reuters provides intelligent information and professional services for law, tax, finance, and media. It serves law firms, corporations, government agencies, and financial institutions with platforms like legal research (Westlaw), tax software (UltraTax CS), financial market data, and news services. These products are accessed through subscription-based platforms, ensuring ongoing updates and a steady revenue stream. The company also monetizes through advertising on media platforms and data analytics sales. It distinguishes itself by offering a broad, integrated suite of industry-specific tools combined with AI, cognitive computing, and machine learning to improve accuracy, efficiency, and predictive analytics in research and data insights. Its goal is to be a trusted partner that helps professionals make informed decisions by delivering reliable information and decision-ready tools while pursuing responsible, values-driven initiatives.

Company size
10,001+
Company stage
IPO
Headquarters
Toronto, Canada
Founded
1851

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