Role: HPC (High-Performance Computing) Consultant Location: Remote Duration: Fulltime (No Contractors) Work Type: Remote-first, with occasional on-site visits to customer offices. Candidates located near any Meta office location will be preferred. We are looking for individual contributor, I've seen resumes with Architect role background, please who working as Lead or Architect not going to suit for this role. So, look for candidates who can do Python coding and strong experience with Kubernetes, Terraform, Hpc. Interview Process: 1st Interview Tech Mahindra: Kubernetes, HPC Technical Interview and Python Coding 2nd Interview Amazon: Kubernetes, HPC Technical Interview and Python Coding 3rd Interview Meta: Kubernetes, HPC Technical Interview and Python Coding Skills Years experience Large-scale production Kubernetes Kubernetes troubleshooting - scheduler/CNI/nodes/upgrades GPU/HPC infrastructure Terraform / IaC Python automation (coding) SRE / reliability / incident management AWS infrastructure AI Infrastructure NVIDIA GPU Platform Calico/Cilium Karpenter Large-scale clusters (500+ nodes) Multi-cloud experience (AWS + Google Cloud Platform + CoreWeave) Must Have: Kubernetes + Slurm + NVIDIA GPU + AI/ML Infrastructure + Terraform + Python + AWS (EKS/FSx Lustre) + Monitoring/SRE We are looking for engineers with expertise across Kubernetes, cloud infrastructure, HPC platforms, GPU computing, Terraform, and automation. Depending on experience, candidates may be considered for Platform Engineer, Kubernetes Engineer, HPC Engineer, Cloud Infrastructure Engineer, DevOps Engineer, AI Infrastructure Engineer, or Site Reliability Engineer (SRE) roles.

Key Responsibilities

Design, deploy, operate, and support large-scale Kubernetes platforms across AWS, Google Cloud Platform, CoreWeave, OCI, and other cloud environments. Manage Kubernetes cluster lifecycle activities including provisioning, scaling, node pool management, upgrades, troubleshooting, and performance optimization. Support AI/ML and HPC workloads, including GPU-enabled compute infrastructure for training and inference environments. Provision and automate cloud and infrastructure resources using Terraform and CI/CD pipelines. Troubleshoot Kubernetes scheduling, networking, storage, and platform reliability issues. Implement monitoring, observability, alerting, SLIs/SLOs, and incident response processes. Collaborate with Networking, Security, Storage, AI/ML, Data Engineering, and Application teams. Develop automation and operational tooling using Python and cloud-native technologies. Participate in production support, root cause analysis, capacity planning, and platform optimization initiatives. Required Skills Kubernetes & Container Platforms Kubernetes (EKS, GKE, AKS, OpenShift, CoreWeave CKS) Cluster Lifecycle Management Node Pool Management Scheduler Troubleshooting CNI Troubleshooting Networking Policies RBAC Helm Autoscaling Rolling Upgrades Cloud Infrastructure AWS (EC2, S3, IAM, VPC, EKS, EFS, FSx for Lustre) Google Cloud Platform (Google Cloud Platform) OCI (Oracle Cloud Infrastructure) Azure (Preferred) Multi-Cloud Infrastructure Infrastructure as Code & Automation Terraform Infrastructure as Code (IaC) CI/CD Pipelines GitHub Actions / Jenkins / GitLab CI Ansible (Preferred) Programming & Scripting Python Bash/Shell Scripting Automation Development REST API Integration HPC & GPU Infrastructure (Preferred) High-Performance Computing (HPC) Slurm NVIDIA GPU Platforms GPU Scheduling CUDA Distributed Computing AI/ML Infrastructure Monitoring & Reliability Prometheus Grafana Datadog Splunk Cloud Monitoring SLI/SLO Management Incident Response Root Cause Analysis (RCA) Preferred Experience Experience with any of the following is highly desirable: AI/ML Infrastructure Platforms Kubeflow KServe Ray MLflow vLLM Vector Databases Distributed Training Platforms CoreWeave AWS ParallelCluster FSx for Lustre Lustre WekaFS InfiniBand RDMA AI Training & Inference Workloads