GPUaaS Kubernetes Platform Engineer

We are seeking a skilled GPUaaS Kubernetes Platform Engineer to design, operate, and support scalable GPU-enabled cloud infrastructure. The role focuses on managing Kubernetes and Open Shift platforms, enabling AI/ML workloads, optimizing GPU resource utilization, and ensuring the reliability and performance of high-performance computing environments.

• Operate and maintain Kubernetes and Open Shift GPU platforms supporting AI, ML, and high-performance computing workloads.
• Configure, deploy, and manage GPU-enabled infrastructure, including accelerator resources, GPU scheduling, and workload optimization.
• Enable and support AI/ML workloads through scalable GPU-as-a-Service (GPUaaS) platforms.
• Implement and maintain CI/CD pipelines for GPU-based applications and infrastructure deployments.
• Manage platform scalability, availability, and performance to ensure reliable GPU service delivery.
• Perform Kubernetes/Open Shift administration, including cluster configuration, upgrades, monitoring, and troubleshooting.
• Develop automation for GPU platform operations, provisioning, and lifecycle management.
• Monitor GPU utilization, capacity planning, and resource optimization across environments.
• Create and maintain operational dashboards, scaling reports, and platform performance metrics.
• Develop and maintain technical documentation, runbooks, troubleshooting guides, and operational procedures.
• Collaborate with AI/ML teams, infrastructure teams, and Dev Ops engineers to deliver enterprise-grade GPU services.

• Strong experience with Kubernetes administration and container orchestration.
• Hands-on experience managing Open Shift environments.
• Experience operating GPU-enabled Kubernetes platforms and accelerator workloads.
• Knowledge of GPU infrastructure concepts, scheduling, and resource management.
• Experience with CI/CD tools and Dev Ops practices.
• Strong understanding of Linux systems, networking, storage, and cloud infrastructure.
• Experience with monitoring, troubleshooting, and performance optimization of distributed platforms.
• Ability to create operational documentation, runbooks, and support procedures.

• Experience with AI/ML infrastructure platforms and MLOps environments.
• Knowledge of GPU technologies such as NVIDIA GPU architectures, CUDA, and GPU operators.
• Experience with infrastructure automation tools such as Terraform, Ansible, or similar technologies.
• Familiarity with cloud platforms and hybrid infrastructure environments.
• Experience supporting large-scale production Kubernetes platforms.