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
