Summary: Join our delivery team as a Middle DevOps Engineer to support stable Kubernetes and Linux foundations for AI and research workloads, optimizing GPU scheduling and automating workflows. Highlights: 1. Support GPU-capable Kubernetes clusters and Linux compute environments. 2. Automate workflows with Python and Bash in a client-facing setting. 3. Optimize GPU scheduling using Kubernetes and Volcano. We are extending our delivery team with a Middle DevOps Engineer to support stable Kubernetes and Linux foundations for AI and research workloads. You will optimize GPU scheduling using Kubernetes and Volcano, enforce quotas, and automate workflows with Python and Bash in a client\-facing setting. Apply today to help scale high\-performance compute platforms **Responsibilities** * Operate and support GPU\-capable Kubernetes clusters and standalone Linux compute environments to maintain efficient scheduling and performance * Configure and maintain Volcano job scheduling, covering queue setup, POD execution, GPU allocation, and namespace quota enforcement * Manage Kubernetes administration end\-to\-end, including namespaces, RBAC, resource quotas, and workload isolation approaches * Create and evolve Python and Shell automation to reduce friction in job submission, resource provisioning, and system reporting * Coordinate with orchestration, optimization, and observability teams to boost scheduling efficiency, capacity utilization, and researcher workflows * Track platform health and resource consumption, sharing metrics and feedback to satisfy optimization and reporting needs * Suggest upgrades to infrastructure, tooling, and automation workflows to enhance performance, scalability, and usability * Support researchers by ensuring a smooth operational experience across 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