AI Infrastructure / Kubernetes Platform Engineer

We are seeking an experienced AI Infrastructure / Kubernetes Platform Engineer to design, deploy, automate, and maintain scalable cloud infrastructure supporting AI/ML and high-performance computing (HPC) workloads. The ideal candidate will have strong hands-on expertise in Kubernetes, cloud infrastructure, Terraform, Python automation, GPU computing, and platform reliability.

This role focuses on building and optimizing cloud-native platforms across AWS and other cloud environments, supporting GPU-accelerated AI workloads, and ensuring infrastructure scalability, performance, availability, and reliability. Experience with Slurm, NVIDIA GPUs, and AWS EKS and FSx for Lustre is highly desirable., * Design, deploy, operate, and maintain large-scale Kubernetes platforms across AWS and multi-cloud environments.

• Manage the complete Kubernetes cluster lifecycle, including provisioning, scaling, upgrades, node pool management, and performance optimization.
• Support AI/ML training and inference workloads running on GPU-enabled infrastructure and HPC platforms.
• Provision and manage cloud infrastructure using Terraform and Infrastructure as Code (IaC) best practices.
• Develop Python and Bash scripts to automate infrastructure operations, deployments, monitoring, and troubleshooting.
• Configure and manage AWS services, including EC2, S3, IAM, VPC, EKS, EFS, and FSx for Lustre.
• Troubleshoot Kubernetes scheduling, networking, storage, autoscaling, and cluster reliability issues.
• Implement and maintain CI/CD pipelines using tools such as GitHub Actions, Jenkins, or GitLab CI.
• Configure monitoring, observability, alerting, and reliability practices using Prometheus, Grafana, Datadog, and related platforms.
• Support HPC workloads and GPU scheduling using technologies such as Slurm and NVIDIA GPU platforms.
• Collaborate with AI/ML, networking, security, storage, and application engineering teams to deliver reliable infrastructure solutions.
• Participate in incident response, root cause analysis, capacity planning, and infrastructure performance optimization.
• Establish and maintain operational standards, infrastructure documentation, security controls, and platform best practices., * Kubernetes & Containers: Kubernetes, EKS, GKE, AKS, OpenShift, Helm, CNI, RBAC, Autoscaling
• Cloud Infrastructure: AWS, EC2, S3, IAM, VPC, EKS, EFS, FSx for Lustre, Google Cloud Platform, OCI, Azure
• Infrastructure Automation: Terraform, IaC, Python, Bash, Ansible, CI/CD, GitHub Actions, Jenkins, GitLab CI
• HPC & GPU Computing: Slurm, NVIDIA GPUs, CUDA, GPU Scheduling, HPC, Distributed Computing
• Monitoring & Reliability: Prometheus, Grafana, Datadog, Splunk, Observability, SLI/SLO, Incident Response, Root Cause Analysis
• AI/ML Infrastructure: Kubeflow, KServe, Ray, MLflow, vLLM, AI Training, AI Inference, Distributed Training

• Strong hands-on experience with Kubernetes and container orchestration platforms, including cluster administration and troubleshooting.
• Experience managing cloud infrastructure, preferably AWS EKS, EC2, VPC, IAM, and FSx for Lustre.
• Proficiency in Terraform and Infrastructure as Code (IaC).
• Strong Python scripting and automation skills.
• Experience with CI/CD pipelines and infrastructure deployment automation.
• Understanding of Kubernetes networking, CNI, RBAC, Helm, autoscaling, node pools, and rolling upgrades.
• Experience with monitoring, observability, incident response, and root cause analysis.
• Strong understanding of cloud networking, storage, security, and infrastructure reliability.
• Ability to troubleshoot complex infrastructure issues and collaborate effectively across technical teams.

Preferred Qualifications:

• Experience with Slurm, NVIDIA GPUs, GPU scheduling, and HPC infrastructure.
• Knowledge of CUDA, distributed computing, and GPU-accelerated AI/ML workloads.
• Experience with multi-cloud platforms, including Google Cloud Platform, OCI, Azure, or CoreWeave.
• Familiarity with AWS ParallelCluster and high-performance storage technologies such as Lustre and WekaFS.
• Experience with AI/ML infrastructure platforms and tools such as Kubeflow, KServe, Ray, MLflow, and vLLM.
• Knowledge of distributed model training, AI inference platforms, InfiniBand, and RDMA.
• Experience with monitoring tools such as Prometheus, Grafana, Datadog, Splunk, and cloud-native monitoring services.
• Familiarity with Ansible, GitHub Actions, Jenkins, or GitLab CI., The ideal candidate combines strong Kubernetes and cloud infrastructure expertise with automation and production reliability experience. Candidates with hands-on exposure to Slurm, NVIDIA GPU infrastructure, and AI/ML workloads will be particularly valuable. The successful candidate should be comfortable managing complex infrastructure, resolving production issues, and building scalable platforms that support modern AI and HPC workloads.