Director, Engineering, AI

Thomson Reuters•Frisco, TX

4d•$158,900 - $295,100•Hybrid

About The Position

The Director of Engineering, AI will lead a global organization of engineering managers, senior engineers, and vendor partners within the Global Westlaw Engineering organization. The role owns the multi-year AI engineering strategy and its alignment with the product delivery roadmap, moves the organization toward mission-based teams with clear ownership and accountability, and advances AI-assisted, agent-driven development. The director is expected to be a hands-on technical leader who uses engineering data to guide decisions, demonstrate results, and help teams innovate. The position leads strategic delivery and the end-to-end AI lifecycle, from retrieval and model integration through evaluation, deployment, and monitoring. It establishes operational standards for AI and non-AI services, including reliability, security, performance, and cost, and champions a DevOps-first approach using automation, CI/CD, and infrastructure as code on AWS. The director also manages project budgets, priorities, staffing, vendor relationships, and decisions about which capabilities should be developed in-house or through partners and vendors. The role promotes responsible, data-driven use of AI across engineering, including coding assistants, agents, code review, test automation, and quality evaluation. It sets guardrails for security, data handling, and human review; analyzes engineering measures such as cycle time, delivery frequency, quality, reliability, and AI tool usage; and manages AI economics, including budgets, token consumption, inference costs, and tooling spend. The director keeps current with models, agent frameworks, protocols such as MCP, and retrieval patterns, and helps teams adopt practices that are production-ready. Thomson Reuters offers a hybrid work environment for office-based roles. Employees may work from anywhere for up to eight weeks per year. The role is posted for proactive recruitment and may be used to fill current or future vacancies.

Requirements

Bachelor's degree in computer science, information technology, or a related field, or equivalent practical experience.
10 or more years of software development experience.
3 or more years leading AI solution development.
10 or more years managing managers and building high-performing engineering teams in an agile environment.
Experience leading managers, individual contributors, and vendor-managed teams, including responsibility for 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.
Track record of shipping LLM-based applications, RAG, or agent workflows in production-grade software, with credible knowledge of prompt and context design, retrieval, evaluation, and production trade-offs.
Strong depth in cloud-native architecture on AWS, CI/CD, infrastructure as code, and operating mission-critical, high-availability systems.
Daily use of AI coding tools, such as GitHub Copilot, Cursor, or Claude Code, and experience empowering teams to adopt AI-assisted development with strong review, quality, and security practices.
Experience defining and analyzing metrics such as DORA, cycle time, quality, and adoption to identify problems and demonstrate results.
Working knowledge of token usage, inference and tooling costs, and measuring return on AI investment.
Experience leading organizational change, introducing tools and new ways of working, and moving teams to mission-based models with clear ownership, decision rights, and accountability.
Excellent communication skills, including the ability to explain technical and financial trade-offs to executives and customers and align stakeholders.

Nice To Haves

Experience leading globally distributed teams and restructuring engineering organizations around products, services, or customer outcomes.
Experience shipping production agentic systems, including evaluation, benchmarking, and monitoring, and knowledge of AI guardrails and governance.
Experience with LLM APIs such as OpenAI or Anthropic at scale, information retrieval or search systems, and standards such as Model Context Protocol (MCP).
Experience with vendor selection, contract negotiation, transitions between vendor and in-house teams, and managing cloud and AI costs through FinOps or inference cost optimization.
Experience building management and technical individual-contributor career paths 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.

Responsibilities

Lead a high-performing global engineering organization, setting expectations and coaching managers and engineers while leading by example with AI tools and engineering data.
Work directly with senior engineers on architecture, technical direction, and complex technical problems.
Own implementation of engineering strategy and its alignment with the product delivery roadmap, maintaining milestones, owners, and measures as priorities evolve.
Transform the organization to long-lived, mission-based teams with clear missions, scope, decision rights, skills, authority, and end-to-end ownership.
Define and regularly review mission measures, including reliability, quality, delivery speed, adoption, and cost, and use results to guide priorities, resourcing, and accountability.
Manage vendor relationships and integrate vendor-managed teams with clear scope, outcomes, service levels, governance, and standards for architecture, quality, security, and delivery.
Ensure vendors document their work and transfer knowledge, and measure vendor performance using the same data and standards as internal teams.
Decide which work to perform in-house, with partners, or through vendors by balancing cost, speed, capability, risk, and long-term knowledge retention.
Communicate proactively with stakeholders and peers, showing business value and shaping product direction.
Deliver strategic projects in partnership with Product Management, UX Design, and Technology teams; remove blockers and make timely decisions.
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 across AI and non-AI services, and champion DevOps practices supported by automation, CI/CD, and infrastructure as code on AWS.
Manage the project portfolio, including budget, priorities, and staff allocation.
Provide guardrails and enable teams to use AI in engineering work, including coding assistants, agents, code review, test automation, and quality evaluation.
Establish responsible AI-use guardrails covering security, data handling, and human review, while encouraging experimentation and adoption.
Analyze engineering data, including GitHub pull request cycle time, review latency, build and deployment frequency, and AI tool usage, to identify bottlenecks and assess changes.
Track ownership and accountability using measures such as incident response, change failure rate, and team cycle time; share results, coach teams, and make practical improvements.
Measure AI's impact 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.
Monitor developments in models, agent frameworks, tool protocols such as MCP, and retrieval patterns; assess production readiness and promote AI/ML engineering practices across the enterprise.
Encourage informed experimentation and risk-taking, and recognize teams and managers who try new approaches.

Benefits

Comprehensive health, dental, vision, disability, and life insurance programs.
401(k) plan with company match.
Flexible vacation and paid time off, including paid holidays and two company-wide mental health days.
Parental leave and sabbatical leave.
Tuition reimbursement.
Employee incentive programs and an employee stock purchase plan.
Flexible Spending and Health Savings Accounts.
Fitness reimbursement and access to the Headspace app and Employee Assistance Program.
Adoption and surrogacy assistance.
Commuter benefits.
Two paid volunteer days annually and opportunities for pro-bono consulting and ESG initiatives.
Flexible hybrid work arrangements, including work from anywhere for up to eight weeks per year.
Career development and continuous learning programming.
Optional hospital, accident and sickness insurance; optional life and AD&D insurance; group legal identity theft protection; and access to a 529 Plan.