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 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.
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 measures such as reliability, quality, delivery speed, adoption, and cost. Help the vendor keep pace with business needs. Hold them to the same standards as internal teams for architecture, quality, security, and delivery, and measure their performance with the same data. Decide what to do in-house, with partners, or through vendors. Balance cost, speed, capability, risk, and long-term knowledge retention.
Deliver strategic projects with urgency. Partner with Product Management, UX Design, and other Technology teams. Drive the end-to-end AI lifecycle. 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. 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 transparent 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.
Measure the impact of AI. Track its effect on delivery speed, code quality, defect rates, and reliability. 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.
Stay on top of the AI landscape. Judge what is production-ready, bring it into your teams' practices, and grow adoption of AI/ML engineering practices and frameworks across the enterprise.
Take informed risks. 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. Shipping AI in production. A track record of shipping LLM-based applications, RAG, or agent workflows in production-grade software. 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.
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

📌 Director, Engineering, Ai (Winnipeg)
🏢 Thomson Reuters
📍 Winnipeg