Director, Engineering, AI

Thomson Reuters Foundation; Eagan (MN); On-site; USD 180,000 - 260,000; Full time

Job summary

Thomson Reuters Foundation seeks a Director, Engineering, AI to lead a global engineering organization focused on AI-powered legal research and workflow capabilities. You will own multi-year AI strategy, align it with product delivery, and drive the shift to AI-assisted development, empowered by data-driven decisions.

You will partner with Product Management, UX Design, and Technology teams to remove blockers, set clear ownership, and steer vendors and internal teams toward strategic outcomes

Qualifications

• Lead and mentor large, global engineering teams.
• Drive AI transformation and multi-year engineering strategy.
• Align AI initiatives with product delivery and business goals.
• Champion AI-enabled, agent-driven development with data-backed decisions.

Responsibilities

• Lead a high-performing global engineering organization.
• Coach managers and engineers; set clear expectations using AI tools and data.
• Work with senior engineers on architecture and technical direction.
• Own the engineering strategy and roadmap alignment with product delivery.

Skills

AI Leadership

People Management

Strategic Planning

Cloud Architecture

DevOps

Data-Driven Decision Making

Tools

AWS

CI/CD

GitHub

MLOps

Job description

Director, Engineering, AI Job Description

Are 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