Summary: Join our delivery team as a Middle DevOps Engineer to run dependable Kubernetes and Linux platforms for AI and research compute, automating GPU orchestration and partnering with clients. Highlights: 1. Run dependable Kubernetes and Linux platforms for AI and research compute 2. Automate and refine GPU orchestration with Kubernetes and Volcano 3. Partner directly with clients to deliver scalable, efficient compute We are growing our delivery team with a Middle DevOps Engineer to run dependable Kubernetes and Linux platforms for AI and research compute. You will automate and refine GPU orchestration with Kubernetes and Volcano, using Python and Shell while partnering directly with clients. Apply now to help deliver scalable, efficient compute environments **Responsibilities** * Deploy and run GPU-enabled Kubernetes clusters and standalone Linux compute environments to keep scheduling and performance efficient * Implement and support Volcano job scheduling, including queue setup, POD execution, GPU allocation, and namespace quota enforcement * Administer Kubernetes platforms end-to-end, including namespaces, RBAC, resource quotas, and workload isolation practices * Build and maintain Python and Shell automation to streamline job submission, resource provisioning, and system reporting * Collaborate with orchestration, optimization, and observability teams to improve scheduling efficiency, capacity utilization, and researcher workflows * Monitor platform health and resource usage, providing data and feedback to satisfy optimization and reporting needs * Propose improvements to infrastructure, tooling, and automation workflows to increase performance, scalability, and usability * Ensure operations deliver a smooth and effective experience for researchers running diverse AI and computational workloads **Requirements** * Hands-on experience with 2+ years in DevOps or infrastructure engineering roles supporting complex, large-scale environments * Expert-level knowledge of Kubernetes administration and orchestration, including namespaces, POD scheduling/distribution, PVC, NFS, and resource quota management * Practical experience with Volcano scheduler for GPU job execution, queue configuration, workload prioritization, and Kubernetes integration * Proven background managing GPU cluster environments in Kubernetes and on standalone Linux compute nodes * Advanced scripting skills in Python for infrastructure automation plus proficiency with UNIX Shell scripting (e.g., Bash) * Strong Linux system administration capability, including troubleshooting, performance tuning, and configuration management * Solid understanding of infrastructure automation and orchestration concepts and related tooling * Fluent English communication skills (spoken and written) for direct client interaction **Nice to have** * Helm for Kubernetes application package management * Monitoring and observability tooling, especially Prometheus, Grafana, and Loki * Infrastructure as Code tools such as Terraform * Multi-cloud Kubernetes exposure, including Amazon EKS and Google GKE * Azure Networking knowledge, including VPN, ExpressRoute, and network security * Familiarity with AI-assisted coding tools (e.g., GitHub Copilot, ChatGPT, Claude) * Experience with hybrid (cloud + on-premises) scheduling and resource optimization