High-Performance Computing Consultant

Carseland, Alberta, Canada
Remote
Posted Oct 29, 2026
1 position

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Employment type: Full-time
Experience level: Senior · 5+ years
Posting language: English
Working hours: 40 hours per week
Location requirements: Country, Carseland, Alberta, Canada
Seniority: Mid-Senior level
Application method: Apply on LinkedIn (LinkedIn sign-in required)

Job summary

Design, deploy, operate, and optimize Kubernetes and cloud infrastructure across multiple providers, supporting HPC and AI/ML workloads, including GPU-enabled environments. Automate infrastructure and operations, troubleshoot platform issues, implement monitoring and reliability practices, and collaborate with cross-functional teams on production support and capacity planning.

Job details

This job posting is for an existing, active vacancy and 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 & 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 (GCP) 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

What you’ll do

Requirements

The role calls for expertise in Kubernetes, cloud infrastructure, Terraform, automation, and HPC or GPU computing, with experience supporting large-scale platforms and workloads. Relevant tools and technologies include AWS, Python, CI/CD pipelines, monitoring systems, and Kubernetes operations; experience with Slurm and AI/ML infrastructure platforms is preferred.