DevOps Engineer, ML Infrastructure

Capgemini

. Build, operate, and evolve large-scale, business-critical infrastructure platforms .

Posted 10/6/2026full-timeRemote • CanadaMid-LevelSenior💰 CA$72,056 - CA$138,940 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in building and managing cloud-native infrastructure for large-scale machine learning workloads, with a strong focus on Kubernetes, CI/CD pipelines, and observability solutions. Proven ability to lead infrastructure modernization initiatives and optimize resource utilization across distributed systems.

Highest-signal resume keywords

Kubernetes ManagementCI/CD Pipeline DevelopmentInfrastructure-as-CodePython ProgrammingObservability Solutions

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

KubernetesPythonGolangTerraformHelmGitHub ActionsJenkinsGitLab CI/CDAWS EKSLinux Systems Administration

Tools & Technologies

VolcanoKueuePrometheusGrafanaAzure DevOps

Industry Keywords

Cloud-Native ArchitectureDistributed SystemsMicroservicesML Lifecycle ManagementInfrastructure Migration

Tech Stack

Tools & technologies

AWSAzureCloudDistributed SystemsGrafanaJenkinsKubernetesLinuxMicroservicesPrometheusPythonTerraformGo

About the role

Key responsibilities & impact

Build, operate, and evolve large-scale, business-critical infrastructure platforms
Develop and manage cloud-native infrastructure supporting large-scale ML workloads on Kubernetes
Implement and operate batch scheduling solutions such as Volcano and Kueue to optimize GPU and compute resource utilization
Build and maintain CI/CD pipelines for ML services, infrastructure, and platform components
Manage and optimize Kubernetes environments, including production workloads on AWS EKS and other cloud platforms
Lead and support infrastructure modernization and migration initiatives across cloud and platform ecosystems
Partner with Data Scientists, ML Engineers, and Software Engineers to productionize machine learning solutions
Implement observability, monitoring, and alerting for distributed systems and GPU clusters
Ensure platform reliability, security, scalability, and operational excellence
Troubleshoot complex distributed systems and performance bottlenecks across infrastructure and ML workloads
Improve developer productivity and drive innovation through automation and AI-assisted operations

Requirements

What you’ll need

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 technologies, cloud-native architectures, and microservices-based platforms
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

Benefits

Comp & perks

Paid time off: Vacation (12–25 days depending on grade), company-paid holidays, personal days, and sick leave
Medical, dental, and vision coverage, or provincial healthcare coordination in Canada
Retirement savings plans, including RRSP in Canada
Life and disability insurance
Employee assistance programs
Other benefits provided by local policy and eligibility
Potential eligibility for variable incentives, bonuses, or commissions