Kubernetes Orchestration

Understands the fundamentals of Kubernetes Orchestration. Applies basic practices in daily work. Follows recommendations from the team and documentation.

Knows Kubernetes basics: pods, services, deployments. Understands how to deploy LLM inference server in K8s and basic resource request concepts for GPU workloads.

Configures Kubernetes for blockchain infrastructure: StatefulSets for nodes with persistent storage, ConfigMaps for chain config. Implements health checks and automatic restart on failures.

Independently deploys LLM services in Kubernetes: configures GPU resource limits, node affinity for GPU nodes, HPA for inference autoscaling. Uses Helm charts for ML workloads.

Designs Kubernetes architecture for production blockchain infrastructure: multi-region deployment, automated failover, rolling node upgrades. Optimizes resource allocation for validator nodes.

Designs Kubernetes infrastructure for LLM platforms: multi-GPU scheduling, model serving with KServe/Triton, distributed training jobs with Kubeflow. Optimizes GPU utilization across the cluster.

Defines Kubernetes strategy for the blockchain organization. Establishes platform team and deployment standards. Coordinates migration to Kubernetes for entire blockchain infrastructure.

Defines Kubernetes strategy for team LLM infrastructure. Establishes ML workload deployment standards, GPU scheduling policies, inference service monitoring and autoscaling.

Defines enterprise orchestration strategy for the blockchain platform. Evaluates Kubernetes vs Nomad vs bare-metal for various blockchain workloads. Shapes platform engineering vision.

Shapes enterprise Kubernetes ML platform strategy. Defines approaches to multi-cluster GPU management, cost optimization, and SLA for inference services at organizational scale.