AWS EKS / AI-ML Platform Engineer

Key Responsibilities

• EKS Platform Operations
• JupyterHub & User Workspace Management
• Workload Scheduling & Compute Management
• AI/ML & Distributed Computing Platform
• GitOps / CI-CD
• Monitoring & Observability
• AWS Service Integration
• Platform Lifecycle & Upgrades
• Security & Access
• Production Support

We are seeking an experienced AWS EKS / AI-ML Platform Engineer to operate, maintain, support, and continuously improve a Kubernetes-based engineering AI/ML platform running on Amazon EKS.

The platform provides engineers and data scientists with an integrated environment for model development, training, workflow orchestration, distributed computing, and GPU/CPU-based workloads. The environment includes JupyterHub, Argo Workflows, Argo CD, Karpenter, Ray/KubeRay, Grafana, Prometheus, Loki, GitHub Actions, Amazon ECR, EFS, S3, RDS PostgreSQL, IAM/IRSA, KMS, and AWS networking/security services.

EKS Platform Operations

• Operate and support the application/platform layer hosted on Amazon EKS.
• Manage Kubernetes namespaces, deployments, services, ingress, Helm releases, configuration, secrets, and platform services.
• Coordinate EKS/Kubernetes lifecycle changes with the AWS/Cloud Operations team.
• Troubleshoot pod scheduling, networking, storage, ingress, authentication, resource constraints, and application availability.
• Maintain platform reliability and operational readiness.

JupyterHub & User Workspace Management

• Administer JupyterHub/JupyterLab environments used by engineering and AI/ML users.
• Maintain user workspace configurations and container images.
• Troubleshoot notebook, pod, storage, image, authentication, and resource-allocation issues.
• Support CPU/GPU-based development and model-training workloads.

Workload Scheduling & Compute Management

• Manage Karpenter policies and Kubernetes compute provisioning.
• Support dynamic CPU/GPU node provisioning and workload scheduling.
• Monitor node utilization, pending workloads, scheduling failures, and capacity constraints.
• Tune resource requests/limits and platform policies to balance performance, reliability, and AWS cost.

AI/ML & Distributed Computing Platform

• Operate Argo Workflows for ML/data workflow orchestration.
• Support Ray/KubeRay distributed computing environments.
• Troubleshoot distributed workloads and worker provisioning.
• Support GPU workloads and associated NVIDIA Kubernetes components.

GitOps / CI-CD

• Administer Argo CD and GitOps-based application deployment.
• Maintain integrations with GitHub / GitHub Actions and Amazon ECR.
• Support controlled promotion of application/container changes across environments.
• Troubleshoot failed deployments, image pulls, Helm releases, sync failures, and configuration drift.

Monitoring & Observability

• Maintain Prometheus, Grafana and Loki monitoring/logging capabilities.
• Build operational dashboards and actionable alerts.
• Monitor: EKS cluster/platform health, Pod and node health, CPU/GPU utilization, Memory utilization, Workload queue/pending time, Karpenter provisioning, Failed workloads, Application/service availability, Storage utilization.
• Support root-cause analysis for platform incidents.

AWS Service Integration

• Operate and troubleshoot platform integrations involving: Amazon EKS, Amazon ECR, Amazon EFS, Amazon S3, Amazon RDS for PostgreSQL, Application Load Balancer / AWS Load Balancer Controller, Route 53, AWS WAF, AWS Certificate Manager, IAM / IRSA, AWS KMS, AWS Systems Manager Parameter Store, VPC endpoints / private connectivity.

Platform Lifecycle & Upgrades

• This should be a major responsibility of the role, rather than an incidental support activity.
• Own lifecycle management of platform Helm charts and application components.
• Plan, test, and execute platform upgrades.
• Track compatibility between EKS/Kubernetes and Karpenter, Argo, JupyterHub, KubeRay, Ray, PrometheGrafana and supporting components.
• Maintain upgrade runbooks and rollback procedures.
• Coordinate platform upgrades with AWS infrastructure teams and application owners.

Security & Access

• Support OIDC-based application authentication.
• Manage Kubernetes RBAC and IAM Roles for Service Accounts (IRSA).
• Maintain secrets and configuration securely.
• Support encryption and certificate management.
• Participate in vulnerability remediation and platform patching.

Production Support

• Provide operational support for platform incidents.
• Perform root-cause analysis and corrective actions.
• Develop operational runbooks and troubleshooting procedures.
• Automate repetitive administrative tasks.
• Work with Cloud Operations, application teams, engineering users, AWS, and other technology teams to resolve complex problems.

Required Technical Skills

• Strong hands-on Kubernetes / Amazon EKS administration
• AWS infrastructure knowledge
• Helm
• Argo CD
• Argo Workflows
• Karpenter
• JupyterHub / JupyterLab
• Kubernetes ingress and AWS Load Balancer Controller
• Prometheus / Grafana
• Git / GitHub and CI/CD
• Docker/container technologies
• Linux administration
• Kubernetes networking and storage
• IAM / IRSA
• ECR, EFS, S3
• Infrastructure troubleshooting
• Python and/or Bash automation

Experience Profile

Recommended level: Senior Platform Engineer / Lead Platform Engineer

I would target approximately: 7+ years infrastructure/platform/DevOps experience, 3+ years hands-on Kubernetes experience, 2+ years supporting production Amazon EKS environments, Demonstrated experience operating production Kubernetes platforms rather than only deploying applications onto Kubernetes.