Working Environment

You'll operate in production-focused environments where platform usability and operational excellence matter. The work sits at the intersection of platform engineering, DevOps and MLOps, with close collaboration across data science, ML engineering, software engineering and architecture.

The role requires comfort working 'in the weeds' - understanding how platforms behave under real workloads, and designing guardrails that balance flexibility for users with reliability, security and governance.

What You'll Be Doing

- Provide technical leadership across platform engineering, DevOps, and MLOps activities

- Design, build, and operate a Kubernetes-based MLOps platform that supports the full model lifecycle

- Implement and operate MLOps tooling and frameworks enabling teams to build, train, deploy, and serve models

- Develop and support model serving and inference capabilities within Kubernetes environments

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