Lead Platform Engineer

Senior, hands-on lead platform engineer responsible for designing, building, and operating an MLOps platform on Kubernetes to enable production AI/ML workloads. You will drive technical direction across platform, DevOps, and MLOps, focusing on reliability, security, and usability. You’ll enable data scientists and ML engineers to experiment, package, deploy, and run models and LLM workloads at scale. This role combines deep technical execution with architectural input to deliver a trustworthy production platform.

• Design, build and operate a Kubernetes-based MLOps platform supporting the full model lifecycle
• Implement and run MLOps tooling for model experimentation, packaging and deployment
• Enable scalable inference and LLM-based workloads
• Operate model serving and inference platforms within Kubernetes
• Collaborate with data scientists and ML engineers to ensure usability and alignment with workflows
• Own platform operability, reliability, security and supportability in production
• Troubleshoot complex issues across Kubernetes, platform services and MLOps layers
• Contribute to architectural decisions while staying hands-on with delivery
• Apply pragmatic engineering judgement under AI workload operational demands

• Senior or Lead Platform Engineer / DevOps Engineer background
• Deep hands-on experience building and operating Kubernetes-based platforms
• Strong Helm and Infrastructure as Code (Terraform) experience
• Proven ability to extend Kubernetes with higher-level platforms and services
• Solid understanding of monitoring, logging, incident response, reliability and maintenance
• Experience working with engineers and data scientists on production workloads
• MLOps experience (building/operating platforms with Kubeflow or similar)
• Experience running model serving and inference platforms (e.g., KServe, vLLM) and notebook environments (e.g., JupyterHub)
• Exposure to emerging tooling for trustworthy AI and security considerations

• Collaborative working style with engineers and data scientists
• Strong problem-solving and pragmatic decision-making
• Clear communication and documentation
• Kubernetes
• Helm
• Terraform