HPC (High-Performance Computing) Consultant

TechDoQuest; United States; Remote; CAD 100,000 - 150,000; Full time

Job summary

TechDoQuest is seeking an HPC Consultant with deep experience in Kubernetes, cloud infrastructure, and HPC platforms. The role involves designing and operating large Kubernetes platforms across AWS and other clouds, supporting AI/ML workloads, and automating resources with Terraform and CI/CD pipelines.

Ideal candidates will have hands-on Kubernetes lifecycle management, GPU infrastructure experience, and strong automation skills using Python and IaC tools.

Qualifications

• Proficient in Kubernetes across multiple environments (EKS, GKE, AKS, OpenShift, CoreWeave).
• Experience with cloud infrastructure and HPC platforms.
• Strong automation and scripting capabilities using Python or similar.

Responsibilities

• Design, deploy, operate, and support large-scale Kubernetes platforms across AWS, GCP, CoreWeave, OCI, and other clouds.
• Manage Kubernetes lifecycle including provisioning, scaling, upgrades, and troubleshooting.
• Support AI/ML and HPC workloads with GPU-enabled compute infrastructure.

Skills

Kubernetes

Cluster Lifecycle

Node Pool

CNI Troubleshooting

RBAC

Autoscaling

AWS

Tools

Terraform

IaC

GitHub Actions

Jenkins

GitLab CI

Ansible

Job description

we are looking for HPC (High-Performance Computing) Consultant immediately with experience in AWS, HPC

Job duties/accountabilities

Location- Remote

We are looking for a individual contributor 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, GCP, 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 (EKS, GKE, AKS, OpenShift, CoreWeave CKS)
• Cluster Lifecycle Management
• Node Pool Management
• CNI Troubleshooting
• RBAC
• Autoscaling
• AWS (EC2, S3, IAM, VPC, EKS, EFS, FSx for Lustre)

Infrastructure as Code & Automation

• Terraform
• Infrastructure as Code (IaC)
• 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)
• NVIDIA GPU Platforms
• GPU Scheduling
• CUDA
• Distributed Computing
• AI/ML Infrastructure

Monitoring & Reliability

• Grafana
• Splunk
• SLI/SLO Management
• Root Cause Analysis (RCA)

Preferred Experience

Experience with any of the following is highly desirable: