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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # AI Infrastructure / Kubernetes Platform Engineer - **Company:** VDart, Inc. - **Location:** United States - **Contract:** Permanent contract - **Skills:** Artificial Intelligence, Amazon Web Services, Amazon Elastic Compute Cloud, Amazon S3, Microsoft Azure, Bash Shell, Cloud Computing, Cloud Engineering, Nvidia CUDA, Continuous Integration, Distributed Computing Environment, Distributed Systems, Github, General-Purpose Computing on Graphics Processing Units, Monitoring of Systems, Identity and Access Management, InfiniBand, Python (Programming Language), Machine Learning, OpenShift, Performance Tuning, Role-Based Access Control, Remote Direct Memory Access, Ansible, Prometheus, AI Infrastructure, Datadog, Scripting, Graphics Processing Unit (GPU), Google Cloud, Cloud Platform System, High Performance Computing, Autoscaling, System Availability, Grafana, Multi-Cloud, Infrastructure as Code (IaC), Amazon Virtual Private Cloud (VPC), Gitlab-ci, Kubernetes, Infrastructure Automation Frameworks, Storage Technologies, Slurm, Machine Learning Operations, Hardware Infrastructure, VLLM, Model Inference, Terraform, Oracle Cloud Infrastructure, Splunk, AWS EKS, KServe, Jenkins - **Published:** October 16, 2026 - **Apply:** https://www.dice.com/job-detail/1596950b-22ed-4c55-a946-fee4cb2a5369 ## About the Role * 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. ## Description 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