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Thomson Reuters

Legal, financial data, and media services

Director, Engineering, AI

Engineering LeadershipConfirmed live in the last 24 hours
Full-time

About the Job

Requirements

  • A bachelor's degree in computer science, information technology, or a related field, or equivalent practical experience.
  • 10+ years in software development, including 3+ years leading AI solution development and 10+ years managing managers and building high-performing engineering teams in an agile environment.
  • Experience leading managers, individual contributors, and vendor-managed teams, including scope, performance, cost, and quality; ability to coach managers on people leadership and engage credibly with Staff and Principal engineers.
  • Experience turning a multi-year vision into programs, roadmaps, and measurable results across multiple teams, including large-scale rollouts and infrastructure initiatives.
  • A track record of shipping LLM-based applications, retrieval-augmented generation, or agent workflows in production-grade software, with knowledge of prompt and context design, retrieval, evaluation, and production trade-offs.
  • Strong depth in cloud-native architecture on AWS, continuous integration and continuous delivery, and infrastructure as code, plus experience running mission-critical, high-availability systems.
  • Daily use of AI coding tools such as GitHub Copilot, Cursor, or Claude Code, and experience enabling teams to adopt AI-assisted development with practices for review, quality, and security.
  • Experience defining and analyzing metrics such as DORA, cycle time, quality, and adoption; working knowledge of token usage, inference and tooling costs, and measuring return on AI investment.
  • Experience shifting team mindsets, introducing tools and ways of working, and moving teams to mission-based, outcome-owned models with clear ownership, decision rights, and accountability.
  • Ability to explain complex technical and financial trade-offs to executives and customers and align stakeholders.

Responsibilities

  • Lead a high-performing global organization by setting expectations, coaching managers and engineers, and using AI tools and engineering data in daily work.
  • Work directly with senior engineers on architecture, technical direction, and complex problems to raise the technical bar across the organization.
  • Own engineering strategy implementation and alignment with the product delivery roadmap, maintaining milestones and owners, adapting to changing priorities, and recommending measures aligned with business strategy.
  • Lead the transformation to a mission-based operating model with long-lived teams owning product areas, services, or customer outcomes from design through production support; define each team's mission, scope, decision rights, skills, and authority.
  • Define measures for each mission, including reliability, quality, delivery speed, adoption, and cost; review them regularly and use them to guide priorities, resourcing, and accountability.
  • Manage vendor relationships, integrate vendor-managed teams with clear scope, outcomes, service levels, and governance, enforce internal standards, measure performance, and ensure documentation and knowledge transfer.
  • Decide which work to perform in-house, with partners, or through vendors, balancing cost, speed, capability, risk, and long-term knowledge retention.
  • Communicate how the organization's work delivers business value and supports strategy, and work directly with stakeholders and peers to shape products.
  • Deliver strategic projects by partnering with Product Management, UX Design, and Technology teams, removing blockers, making timely decisions, and enabling teams to solve complex business problems with defined outcomes.
  • Own the end-to-end AI lifecycle, from retrieval and model integration through evaluation, deployment, and monitoring.
  • Set operational standards for reliability, security, performance, and cost for AI and non-AI services; champion a DevOps-first model using automation, continuous integration and delivery, and infrastructure as code on AWS.
  • Own the project portfolio, including budget, priorities, and staff allocation.
  • Establish guardrails for responsible AI use covering security, data handling, and human review of AI-generated code and outputs; enable experimentation and adoption of AI tools in engineering work.
  • Analyze GitHub data, including pull request cycle time, review latency, build and deployment frequency, and AI tool usage, to identify bottlenecks and measure change.
  • Use incident response, change failure rate, and team cycle time to track ownership and accountability; share results, coach teams, and turn findings into practical changes.
  • Measure AI's effects on delivery speed, code quality, defect rates, and reliability, and adjust strategy and investment accordingly.
  • Set AI budgets and optimize token consumption, inference costs, and tooling spend; guide model selection and cost-versus-quality trade-offs.
  • Track new models, agent frameworks, tool protocols such as Model Context Protocol, and retrieval patterns; assess production readiness and promote AI and machine learning engineering practices and frameworks across the enterprise.
  • Encourage informed experimentation and recognize teams and managers who try new approaches.

Desired Qualifications

  • Experience leading globally distributed teams and restructuring an engineering organization around products, services, or customer outcomes.
  • Experience shipping agentic systems in production, including evaluation, benchmarking, and monitoring, and knowledge of AI guardrails and governance.
  • Experience with large language model APIs such as OpenAI or Anthropic at scale, information retrieval or search systems, and standards such as Model Context Protocol.
  • Experience with vendor selection, contract negotiation, and transitions between vendor and in-house teams; experience managing cloud and AI costs through FinOps or inference cost optimization.
  • Experience building career paths for management and technical individual-contributor tracks and retaining senior technical talent.
  • Strong Python and RESTful API design skills; AWS certifications such as Solutions Architect or DevOps Engineer.
  • Background in legal or other regulated, content-rich professional domains.

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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