Remote Senior DevOps Engineer, ML Infra on Kubernetes

Capgemini; Toronto; Remote; CAD 103,000 - 198,000; Full time

Benefits offered by this job

Paid time off

Medical, dental, vision

Retirement savings plans

Life and disability insurance

Employee assistance programs

Job summary

Capgemini Engineering is seeking a Senior DevOps Engineer to build and operate large‑scale, reliable infrastructure for ML workloads. The role emphasizes automation, scalability, and observability across distributed systems.

You will collaborate with data scientists, ML engineers, and software engineers to productionize machine learning solutions on Kubernetes with AWS EKS and other clouds, while modernizing infrastructure and driving continuous improvement.

Qualifications

• Strong experience with Kubernetes and large‑scale workload orchestration.
• Hands‑on experience with Kubernetes batch schedulers such as Volcano and Kueue.
• Solid understanding of distributed systems, containerization, cloud‑native architectures and microservices.
• Experience managing workloads on Kubernetes platforms such as AWS EKS.
• Proven track record delivering and supporting infrastructure migration projects.
• Strong programming skills in Python or Golang.
• Experience with Infrastructure‑as‑Code and deployment tools including Terraform and Helm.
• Experience designing and maintaining CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI/CD, or Azure DevOps.
• Strong Linux systems administration and troubleshooting skills.
• Experience building observability solutions using Prometheus and Grafana.
• Understanding of ML lifecycle management, model deployment, and production operations.

Responsibilities

• Develop and manage cloud‑native infrastructure for large ML workloads on Kubernetes.
• Operate batch scheduling with Volcano/Kueue to optimize GPU and compute resources.
• Build and maintain CI/CD pipelines for ML services and platforms.
• Manage Kubernetes environments across AWS EKS and other clouds.
• Lead infrastructure modernization and migration initiatives across cloud ecosystems.
• Collaborate with Data Scientists, ML Engineers, and Software Engineers to productionize ML solutions.
• Implement observability, monitoring, and alerting for distributed systems and GPU clusters.
• Ensure platform reliability, security, scalability, and excellence.
• Troubleshoot complex distributed systems and performance bottlenecks.

Skills

Kubernetes

Distributed systems

Python or Golang

Linux administration

Tools

Volcano

Kueue

Terraform

Helm

GitHub Actions

Jenkins

GitLab CI/CD

Azure DevOps

AWS EKS

Prometheus

Grafana

Job description

Capgemini Engineering is seeking a Senior DevOps Engineer to build and operate large‑scale, reliable infrastructure for ML workloads. The role emphasizes automation, scalability, and observability across distributed systems.

You will collaborate with data scientists, ML engineers, and software engineers to productionize machine learning solutions on Kubernetes with AWS EKS and other clouds, while modernizing infrastructure and driving continuous improvement.