Remote MLOps Platform Engineer - AWS EKS & GPU

Capgemini; United States; Remote; USD 100,000,000 - 156,165,000; Part time

Benefits offered by this job

Medical benefits

Dental benefits

Vision benefits

Retirement benefits

Job summary

Capgemini seeks a DevOps Engineer to design and scale cloud-native infrastructure for AI/ML workloads with a strong focus on Kubernetes, EKS, and MLOps. You will automate provisioning, build CI/CD pipelines, and optimize GPU clusters across distributed systems.

Collaboration with data scientists and software engineers will drive productionizing AI/ML solutions. Ideal candidates have 5+ years in DevOps, hands-on Kubernetes, and IaC expertise, plus experience with Terraform, Helm, and

Qualifications

• 5+ years in DevOps/SRE/Platform/Infrastructure roles.
• 3+ years supporting production AI/ML platforms.
• Hands-on Kubernetes and workload orchestration.
• Experience with Volcano and/or Kueue batch schedulers.
• Production workloads on AWS EKS.
• Python and/or Golang programming.
• Terraform, Helm, IaC methodologies.
• CI/CD pipelines with GitHub Actions, Jenkins, GitLab CI/CD, or Azure DevOps.
• Linux administration and troubleshooting.
• Monitoring/observability with Prometheus and Grafana.
• Distributed systems, cloud-native architecture, microservices.
• ML lifecycle management, model deployment, production ops.

Responsibilities

• Design, build, and maintain scalable MLOps platforms for training, deployment, and monitoring of machine learning models.
• Develop and manage cloud-native infrastructure supporting large-scale ML workloads on Kubernetes.
• Implement and operate GPU workload scheduling solutions such as Volcano and Kueue.
• Build and maintain CI/CD pipelines for ML services, infrastructure, and platform components.
• Automate infrastructure provisioning and lifecycle management using Terraform, Helm, and IaC practices.
• Manage and optimize Kubernetes environments, including AWS EKS production clusters.
• Lead infrastructure modernization and cloud migration initiatives.
• Partner with Data Scientists, ML Engineers, and Software Engineers to productionize AI/ML solutions.
• Implement monitoring, alerting, and observability across distributed systems and GPU clusters.
• Troubleshoot performance, scalability, and reliability issues across ML platforms and infrastructure.

Skills

Kubernetes

AWS EKS

CI/CD

Python

Golang

Terraform

Helm

GitHub Actions

Jenkins

GitLab CI/CD

Azure DevOps

Linux

Prometheus

Grafana

Kubeflow

MLflow

Distributed Systems

Containerization

Microservices

Tools

Kubeflow

MLflow

Job description

Capgemini seeks a DevOps Engineer to design and scale cloud-native infrastructure for AI/ML workloads with a strong focus on Kubernetes, EKS, and MLOps. You will automate provisioning, build CI/CD pipelines, and optimize GPU clusters across distributed systems.

Collaboration with data scientists and software engineers will drive productionizing AI/ML solutions. Ideal candidates have 5+ years in DevOps, hands-on Kubernetes, and IaC expertise, plus experience with Terraform, Helm, and