Kubernetes for AI Workloads Specialist

Running AI workloads on Kubernetes unlocks powerful scheduling, isolation, and scaling capabilities — but GPU-accelerated workloads introduce unique configuration challenges that standard Kubernetes knowledge doesn't cover. The Kubernetes for AI Workloads Specialist helps platform engineers configure, tune, and operate Kubernetes clusters optimized for machine learning training jobs, inference deployments, and data processing pipelines.

This assistant addresses the specific challenges that arise when you bring GPU workloads into a Kubernetes environment. It starts with the foundational layer: NVIDIA GPU Operator installation and configuration, device plugin setup, time-slicing vs. MIG (Multi-Instance GPU) partitioning strategies, and how to expose GPU resources correctly to pods. It covers the common misconfigurations that cause GPU jobs to be unschedulable or to interfere with each other in multi-tenant environments.

Scheduling is a major focus area. The assistant covers node affinity rules for GPU node pools, pod topology spread constraints for distributed training jobs, Kubernetes job controllers appropriate for ML workloads (standard Job, indexed Job, Kubeflow's MPI Operator, PyTorchJob, and Volcano for gang scheduling). Gang scheduling is particularly important for distributed training — the assistant explains why standard Kubernetes scheduling breaks for multi-node jobs and how to configure Volcano or the Coscheduling plugin to solve it.

Resource management and multi-tenancy are covered in depth: namespace resource quotas for GPU resources, priority classes for production vs. research workloads, cluster autoscaler configuration for GPU node pools (including the latency implications of cold-starting GPU instances), and Karpenter as an alternative for faster node provisioning. It also covers storage for AI workloads: ReadWriteMany persistent volumes for shared datasets, CSI drivers for high-performance storage (Lustre, GPFS), and ephemeral storage sizing for large model artifacts.

This role is used by DevOps and platform engineers building or operating AI-dedicated Kubernetes clusters, MLOps engineers deploying model training and serving infrastructure, and cluster administrators managing shared GPU resources across multiple teams.