HPC (High-Performance Computing) Consultant

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., * 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.

1. 1st Interview Tech Mahindra: Kubernetes, HPC Technical Interview and Python Coding
2. 2nd Interview Amazon: Kubernetes, HPC Technical Interview and Python Coding
3. 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, 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., 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