Jobs / Capgemini Engineering
Kubernetes (K8S) / HPC Engineer
Capgemini Engineering · Toronto, ON, Canada
Toronto, ON, CanadaExp: 5+ yrs72,056-138,940 CAD/yearlyRemote
Remuneration
May be eligible for variable incentives, bonuses, or commissions; benefits may include paid time off, healthcare coverage, retirement savings plans, life and disability insurance, and employee assistance programs.
Location
Toronto, ON, Canada
Visa sponsorship
Not specified
Job summary
Designs, deploys, and supports scalable Kubernetes and high-performance computing platforms for AI/ML, simulation, and other compute-intensive workloads across cloud and on-premises environments. The role manages containerized workloads and HPC schedulers, optimizes NVIDIA GPU infrastructure, and builds automation, CI/CD, monitoring, and performance solutions. It collaborates with platform, research, and AI teams to maintain reliable, secure, and high-performing infrastructure.
Benefits
Paid time off, including vacation, holidays, personal days, and sick leaveMedical, dental, and vision coverage or provincial healthcare coordinationRetirement savings planLife and disability insuranceEmployee assistance program
Qualifications
- At least 5 years of experience in Kubernetes, cloud infrastructure, platform engineering, DevOps, or a related field.
- At least 2 years of experience supporting HPC or large-scale distributed compute environments.
- Hands-on experience with Kubernetes, Docker, Helm, and container orchestration.
- Experience managing HPC clusters and workload schedulers such as Slurm or Volcano.
- Expertise with NVIDIA GPU infrastructure, GPU-enabled workloads, and performance optimization.
- Strong Linux administration and troubleshooting skills.
- Proficiency in Python or Bash scripting.
- Experience with CI/CD pipelines, monitoring, logging, and infrastructure automation.
- Hands-on experience with a major cloud platform, including Azure AKS or Google Kubernetes Engine.
- Exposure to Trainium or Inferentia is preferred.
Responsibilities
- Design, deploy, and manage Kubernetes compute platforms for AI/ML and HPC workloads.
- Build and maintain scalable HPC environments using workload schedulers.
- Support and optimize NVIDIA GPU infrastructure for machine learning, data processing, and simulation.
- Implement infrastructure automation, CI/CD pipelines, and monitoring solutions.
- Manage containerized applications with Kubernetes, Docker, and Helm.
- Troubleshoot and optimize cluster, network, storage, and application performance.
- Collaborate with engineering, research, and AI teams on HPC and cloud-native solutions.
- Maintain platform scalability, availability, security, and operational reliability.
Skills
AKSAzureBashDockerGKEHelmKubernetesLinuxPython
Industry
Engineering services
Relocation
No