Summary: Join a client-facing team as a Middle DevOps Engineer, improving Kubernetes and Linux platforms with a focus on GPU scheduling and automation for advanced AI workloads. Highlights: 1. Build dependable Kubernetes and Linux platforms for advanced AI workloads 2. Administer GPU-enabled Kubernetes clusters and implement Volcano scheduling 3. Develop Python and Shell automation for infrastructure and job submission We are building dependable Kubernetes and Linux platforms, with a focus on GPU scheduling and automation at scale. As a **Middle DevOps Engineer**, you will run and improve Kubernetes environments (including Volcano) and the underlying Linux infrastructure using Python and Shell scripting in a client\-facing delivery team. Apply to help deliver efficient, reliable compute environments for advanced AI workloads. **Responsibilities** * Deploy, configure, and operate GPU\-enabled Kubernetes clusters and standalone Linux compute environments to keep scheduling and performance optimized * Implement and administer Volcano job scheduling, including queue setup, POD execution, GPU allocation, and namespace quota enforcement * Administer Kubernetes end to end, covering namespaces, RBAC, resource quotas, and workload isolation approaches * Create and maintain Python and Shell automation to simplify job submission, resource provisioning, and system reporting * Collaborate with orchestration, optimization, and observability teams to raise scheduling efficiency, improve capacity utilization, and streamline researcher workflows * Monitor infrastructure health and resource utilization, supplying data and feedback for optimization and reporting needs * Identify opportunities to enhance infrastructure, tooling, and automation workflows to improve performance, scalability, and usability * Ensure operational processes provide a smooth and efficient experience for researchers running diverse AI and computational workloads **Requirements** * Hands\-on background with 2\+ years of experience in DevOps or infrastructure engineering within complex, large\-scale environments * Expertise in Kubernetes administration and orchestration, including namespaces, POD scheduling/distribution, PVC, NFS, and resource quota management * Practical experience with the Volcano scheduler for GPU job execution, queue configuration, and workload prioritization integrated with Kubernetes * Proven ability to operate GPU cluster environments in Kubernetes as well as on standalone Linux compute nodes * Advanced Python scripting skills for infrastructure automation, plus proficiency in UNIX Shell scripting such as Bash * Strong Linux system administration skills, 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** * Knowledge of Helm package management for Kubernetes applications * Familiarity with monitoring and observability solutions, particularly Prometheus, Grafana, and Loki * Skills in Infrastructure as Code tools such as Terraform * Background in multi\-cloud Kubernetes environments including Amazon EKS and Google GKE * Understanding of Azure Networking including VPN, ExpressRoute, and network security * Familiarity with AI\-assisted coding tools such as GitHub Copilot, ChatGPT, and Claude * Experience with hybrid (cloud and on\-premises) scheduling and resource optimization