engineering
Posted Apr 14Manager, Software Development Engineering - AI Platform
at Workday
Vancouver, CanadaRemote
Responsibilities
- Drive execution of the team’s roadmap, ensuring high-quality, timely delivery of platform capabilities.
- Collaborate with cross-functional partners (product, AI/ML, infrastructure) to define
- Establish best practices for code quality, testing, observability, and operational excellence.
- Build a strong engineering culture focused on ownership, collaboration, and continuous improvement.
- Manage team health, hiring, performance, and career development. About You Basic
- Track record of building and growing high-performing engineering teams.
- Lead, mentor, and grow a team of engineers building the Agent Platform.
Requirements
- We’re obsessed with making hard work pay off, for our people, our customers, and the world around us. As a Fortune 500 company and a leading AI platform for managing people, money, and agents, we’re shaping the future of work so teams can reach their potential and focus on what matters most.
- About the Team Do you want to build impactful, AI features and solutions that will be used by millions of end-users? We are in the AI Platform organization at Workday and we solve meaningful problems that lie at the intersection of machine learning and enterprise-scale software! We build advanced AI solutions that power the core Workday software by modeling user behavior and providing intelligent automation.
- About the Role We are looking for an Engineering Manager to lead a team building our Agent Platform—the core infrastructure that enables teams to develop, deploy, orchestrate, and operate AI agents in production.
- You’ll partner closely with product, applied AI, and infrastructure teams to define how agent systems are built and operated across the organization.
- experience working with Python (or similar languages such as Java or Go). Other
- Experience leading teams working on platforms, infrastructure, or developer tooling.
- Experience delivering complex technical projects in ambiguous, fast-moving environments.
- Familiarity with AI/ML systems or LLM-powered applications in production.
- Familiarity with Kubernetes and cloud-native systems.
- Experience with workflow orchestration, distributed pipelines, or complex multi-step systems.