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