Senior Platform Engineer

In this role you will design, build, and operate a production-grade Kubernetes-based AI/ML platform that enables ML and data science teams to deploy and run models at scale. You will create tooling, workflows, and guardrails to support reliable ML workloads, not only run clusters. You’ll collaborate with data scientists and MLOps engineers to ensure the platform is practical and fit-for-purpose, while owning platform reliability, security, and lifecycle management. This is a hands-on position with impact on architecture and operational excellence. You’ll work at the intersection of infrastructure and ML to shape scalable AI delivery.

• Design, build, and operate a Kubernetes-based platform supporting multiple ML and engineering teams
• Extend Kubernetes with MLOps-specific capabilities
• Provide platform-level support for model development, packaging, deployment, and promotion
• Build shared platform services for repeatable model deployment
• Collaborate with data scientists and MLOps engineers to ensure usability and fit-for-purpose
• Own platform operability, reliability, security, and lifecycle management in production
• Troubleshoot cross-layer issues across infrastructure, Kubernetes, and MLOps
• Contribute to architectural decisions while remaining hands-on
• Work on scalable inference and LLM-based workloads

• Strong background as a Senior Platform Engineer or Senior DevOps Engineer
• Hands-on experience building and operating Kubernetes-based platforms
• Proficiency with Helm and Infrastructure as Code (e.g., Terraform)
• Experience building internal platforms for other engineers
• Solid grasp of monitoring, logging, reliability, incidents, and maintainability
• Ability to collaborate closely with MLOps engineers and data scientists
• ML platform and MLOps knowledge (practical understanding of ML/AI workloads in production)
• Exposure to MLOps platforms (e.g., Kubeflow) and model serving/inference platforms (e.g., KServe, vLLM)
• Experience with notebook environments (e.g., JupyterHub)
• Awareness of tools around Responsible/Trustworthy AI

• Collaborative mindset
• Practical problem-solving
• Clear communication across cross-functional teams
• Kubernetes
• Helm
• Terraform