• Build Enterprise-Scale Infrastructure • Leverage infrastructure-as-code to manage complex cloud environments supporting critical ML and AI initiatives. • Design Kubernetes-native systems, including controllers/operators where appropriate. • Improve platform networking, security, and observability • Sustain Platform Health and Performance • Own critical systems in production, including reliability, scalability, security, and cost efficiency. • Identify and proactively address technical debt, operational risk, and platform bottlenecks. • “Learn by doing” — Quickly ramp up to a complex tech stack (Terraform, Kubernetes, Istio, Crossplane, Go, TypeScript) • Enable Teams and Customers to Move Faster • Create abstractions and tooling that make it easier for teams and customers to deploy, run, and scale AI/ML workloads. • Collaborate directly with customers to understand their ML infrastructure challenges and translate them into platform improvements. • Balance speed and rigor—shipping quickly while maintaining a high bar for quality and safety. • Lead Through Influence • Act as a technical leader and mentor across the engineering organization. • Write clear documentation and design proposals that align stakeholders and drive decisions. • Partner closely with product and leadership to shape platform direction and priorities.