Director, Engineering, AI
Thomson Reuters · United States, Eagan, Minnesota · hybrid
Director, Engineering, AIJob DescriptionAre you passionate about transforming how legal professionals find trusted answers with AI? Join our Global Westlaw Engineering organization and help shape the future of AI-driven legal research.
Westlaw helps legal professionals around the world research, analyze, and act with confidence. We are building the next generation of fiduciary-level, AI-powered legal research and workflow capabilities, and we are changing how we build software.
As Director, you will lead a global organization of engineering managers, senior engineers, and vendor partners. You will own the multi-year AI engineering strategy and its alignment with the product delivery roadmap. You will move the organization to mission-based teams with clear ownership and accountability. You will also drive the shift to AI-assisted, agent-driven development and use engineering data to guide decisions and show results.
We want a technical leader with a track record of leading AI transformation, who leads by example, stays hands-on, keeps up with AI and engineering best practices, uses data to find what needs to change, and empowers teams to innovate across the organization.
Key Responsibilities:Lead
- Lead a high-performing global organization. Set clear expectations, coach managers and engineers, and lead by example, using AI tools and engineering data in your own work.
- Stay close to the technical work. Work directly with senior engineers on architecture, technical direction, and hard problems to raise the technical bar across the organization.
- Own Engineering strategy implementation and its alignment with the product delivery roadmap. Run it as a living plan with clear milestones and owners, adapt quickly as priorities evolve, and recommend measures that keep engineering objectives aligned to business strategy.
- Lead the transformation to a mission-based operating model. Replace project-by-project handoffs with long-lived teams that own a product area, service, or customer outcome from design through production support. Define each team's mission, scope, decision rights, and the skills and authority it needs.
- Set measures for each mission. Define measures such as reliability, quality, delivery speed, adoption, and cost. Review them regularly and use them to guide priorities, resourcing, and accountability.
- Manage the vendor relationship. Help the vendor keep pace with business needs. Integrate vendor-managed teams into the organization, with clear scope, outcomes, service levels, and governance. Hold them to the same standards as internal teams for architecture, quality, security, and delivery, and measure their performance with the same data. Make sure they document their work and transfer knowledge, so the organization does not depend on a single vendor.
- Decide what to do in-house, with partners, or through vendors. Balance cost, speed, capability, risk, and long-term knowledge retention.
- Communicate proactively. Show how your organization's work delivers business value and supports strategy, and work directly with stakeholders and peers to shape the future of our products.
Deliver
- Deliver strategic projects with urgency. Partner with Product Management, UX Design, and other Technology teams. Remove blockers, make timely decisions, and enable your teams to solve complex business problems with clearly defined outcomes.
- Drive the end-to-end AI lifecycle. Own the work from retrieval and model integration through evaluation, deployment, and monitoring.
- Set the standard for operational excellence. Hold AI and non-AI services to high standards for reliability, security, performance, and cost. Champion a DevOps-first model supported by automation, CI/CD, and infrastructure as code on AWS.
- Manage the project portfolio. Own budget, priorities, and staff allocation.
Operate: Run a Data-Driven, AI-Enabled Organization:
- Provide guardrails and empower teams to use AI. Equip managers, engineers, and teams to apply AI in daily engineering work, including coding assistants, agents, code review, test automation, and quality evaluation. Provide clear guardrails for responsible use, covering security, data handling, and human review of AI-generated code and outputs. Within those guardrails, encourage teams to experiment and adopt AI, and remove the barriers that slow them down.
- Make decisions with data. Analyze GitHub data, such as pull request cycle time, review latency, build and deployment frequency, and AI tool usage, to find bottlenecks and show where change is working.
- Track ownership and accountability. Use incident response, change failure rate, and cycle time by team to show whether ownership and accountability are improving. Share results openly, use them to coach rather than blame, and turn them into practical changes in how teams work.
- Measure the impact of AI. Track its effect on delivery speed, code quality, defect rates, and reliability. Use those measures to adjust strategy and investment.
- Own the economics of AI usage. Set AI budgets, and track and optimize token consumption, inference cost, and tooling spend. Guide model selection and cost-versus-quality trade-offs, balancing them against productivity, quality, and speed.
Innovate
- Stay on top of the AI landscape. Follow new models, agent frameworks, tool protocols such as MCP, and retrieval patterns. Judge what is production-ready, bring it into your teams' practices, and grow adoption of AI/ML engineering practices and frameworks across the enterprise.
- Try new approaches. Take informed risks. Encourage your managers and teams to do the same, and recognize those who do.
Required Qualifications
- Education. A bachelor's degree in computer science, information technology, or a related field, or equivalent practical experience.
- 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.
- Organizational leadership. Experience leading managers, individual contributors, and vendor-managed teams, including scope, performance, cost, and quality. You can coach managers on people leadership and engage credibly with Staff and Principal engineers.
- Strategy and execution. Experience turning a multi-year vision into programs, roadmaps, and measurable results across multiple teams, including large-scale rollouts and infrastructure initiatives.
- Shipping AI in production. A track record of shipping LLM-based applications, RAG, or agent workflows in production-grade software. You can speak credibly about prompt and context design, retrieval, evaluation, and production trade-offs.
- Cloud, DevOps, and reliability. Strong depth in cloud-native architecture on AWS, CI/CD, and infrastructure as code, and experience running mission-critical, high-availability systems.
- Hands-on AI tool use. Daily use of AI coding tools (e.g., GitHub Copilot, Cursor, Claude Code). You lead by example and have empowered managers and teams to adopt AI-assisted development, with strong practices for review, quality, and security.
- Data-driven leadership and AI economics. Experience defining and analyzing metrics such as DORA, cycle time, quality, and adoption to find problems and show results. Working knowledge of token usage, inference and tooling costs, and how to measure the return on AI investment.
- Leading change. Experience shifting team mindsets, introducing new tools and ways of working, and moving teams to mission-based, outcome-owned models with clear ownership, decision rights, and accountability.
- Communication. Excellent communication skills. You can explain complex technical and financial trade-offs to executives and customers and align stakeholders.
Preferred Qualifications
- Experience leading globally distributed teams and restructuring an engineering organization around products, services, or customer outcomes.
- Agenti