Jobs at Thomson Reuters

Design, develop, deploy, and support scalable cloud-native backend services, REST APIs, microservices, and distributed applications. Own solutions across the full software development lifecycle, including database optimization, automated testing, observability, troubleshooting, and production support. Collaborate with cross-functional teams on architecture and technical decisions, improve platform reliability and performance, and mentor engineers through code reviews, guidance, and knowledge sharing.

Build and maintain predictive models, including propensity-to-upgrade and engagement scoring, to drive AI product adoption and customer expansion. Develop features from product, behavioral, campaign, learning, and firmographic data. Partner with Marketing, Customer Success, Data Engineering, Product, and Commercial teams to operationalize models in Treasure Data and activation workflows. Monitor model performance, support experimentation, measure lift, document methodologies, and communicate insights to technical and nontechnical stakeholders.

Leads an AI-first product operations office across Thomson Reuters’ TAAP portfolio. The role automates coordination, dependency tracking, reporting, and communications; advances AI maturity through shared infrastructure, agentic workflows, playbooks, and modern product development practices; and drives organization-wide change. Responsibilities include managing a senior product operations team, enabling product managers, coordinating cross-functional commitments, supporting customer migrations, establishing business metrics, and leading communications and cultural transformation across product, engineering, design, and customer success.

Lead backend and AI orchestration systems for CoCounsel Audit: design and operate scalable, low-latency AI workflows, integrate LLMs and retrieval systems, mentor engineers, set platform strategy, and ensure reliability, observability, and auditability at production scale.

Lead the engineering team for a search platform at Thomson Reuters, focusing on building, operating, and evolving the infrastructure while managing a team of software engineers and aligning efforts with business priorities.

Lead product strategy and roadmap to enter Financial Services for Noetica. Define customer requirements, design data-intensive features, partner with engineering, GTM and sales, and drive commercial growth and integrations to scale the product for fund managers, credit managers, and treasuries.

Lead architecture and implementation of AI-first backend and orchestration systems for Audit Suite. Drive scalable, reliable AI workflows (LLM integration, retrieval, vector stores), mentor engineers, set technical direction, and collaborate across product, design, and ML to deliver auditable, high-throughput document-centric solutions.

Lead the Customer Files Orchestration engineering team and own its distributed service end to end, including file upload, storage, extraction, embedding, indexing, APIs, queuing, reliability, and operations. Hire, coach, and develop engineers; establish team processes, on-call, SLOs, and incident practices; set technical direction and roadmap; and partner with product, platform, storage, document understanding, and indexing teams to scale a reliable, API-first service.

The Lead Data Engineer will architect and develop reporting solutions, integrate AI, analyze complex datasets, and mentor team members to enhance data-driven decision-making.

As a Senior Software Engineer, Full Stack, you will build backend services and APIs for a document analysis platform, focusing on server-side architecture and data processing, while also contributing to frontend features as needed.

Supports commercial strategy and product pre-onboarding by defining operating models, mapping current and future processes, documenting requirements, analyzing capability gaps, and coordinating cross-functional stakeholders. The role creates process maps, user stories, governance materials, status reports, and implementation handoff packages while tracking risks, dependencies, decisions, and approvals across multiple initiatives.

Lead the development of AI-powered solutions, drive technical strategy and innovation, mentor engineers, and ensure quality and operational excellence.

Lead strategy, architecture, development, integration, and full lifecycle implementation of Conga CPQ, Turbo Price, Digital Commerce, and PROS Pricing solutions on Salesforce. Establish engineering standards, conduct code reviews, mentor developers, evaluate technologies, resolve complex issues, and ensure scalable, stable systems. Collaborate with architects, technical leads, cross-functional teams, and third parties while managing estimates, dependencies, risks, Agile delivery, and AI-enabled Agentforce capabilities.

Lead end-to-end change management for a multi-year commercial systems transformation: design strategy, templates, adoption metrics, and governance; oversee readiness across workstreams, engage stakeholders, advise launch decisions, and mentor a team to drive organizational adoption.

Lead and manage forward-deployed engineering teams to design, build, and deliver scalable, secure LLM/AI solutions for customers. Provide technical architecture guidance, drive MLOps/LLMOps best practices, oversee delivery of POCs and production systems, hire and develop engineers, and collaborate with Product, Sales, and Customer Success to shape product roadmaps and reusable AI frameworks.

Build and productionize AI agents for product analytics, integrating LLMs, tools, retrieval pipelines, and data systems. Develop data pipelines, evaluation frameworks, guardrails, telemetry, and monitoring. Diagnose agent quality issues, improve prompts and context, and contribute to proactive product intelligence systems. Collaborate with product and engineering teams to translate business needs into reliable agent designs while documenting work and following shared development standards.

Owns the build, hardening, patching, configuration, and lifecycle management of enterprise operating-system and platform infrastructure. Leads major incidents, root-cause analysis, change control, service improvement, and technical escalations. Identifies and scales AI and automation for incident triage, runbook execution, predictive monitoring, and reduced MTTR. Provides solution designs, implementation plans, standards, documentation, stakeholder influence, team mentoring, vendor coordination, and AI governance.

Leads a platform engineering team responsible for backend services, AI agent infrastructure, CI/CD, cloud infrastructure, observability, and operational reliability. Manages hiring, performance, career development, roadmap planning, architecture, production operations, incident response, and on-call participation. Guides scalable microservices and event-driven systems running on AWS and Kubernetes while partnering with product, applied AI, and infrastructure teams. Raises engineering standards across testing, deployment, monitoring, and maintainability.

Lead architecture and delivery of production AI agent systems: design agent infrastructure, observability, evaluation, progressive rollout, and reliability. Provide hands-on technical leadership, mentor senior engineers, drive RFCs/ADRs, and partner with product to sequence strategic platform work.

The Senior Applied Scientist will design, build, and deploy AI solutions for document understanding in the legal domain, leveraging NLP and deep learning methodologies. This role involves collaborating with product teams, advancing model capabilities, and ensuring robust training and evaluation methodologies.