DevOps Engineer - ML Infrastructure

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

Remote

Contract (5 months 30 days)

Published 6 days ago

mlflow

distributed systems

mlops

CI/CD

AWS EKS

terraform

GPU Infrastructure

Python (Django, Flask, FastAPI), ORM, SQL

Role Overview

We are seeking a DevOps Engineer with deep expertise in cloud-native infrastructure, Kubernetes, and MLOps to build and support enterprise-scale machine learning platforms. This role is responsible for designing, automating, and optimizing the infrastructure that powers AI/ML workloads, enabling efficient model training, deployment, monitoring, and lifecycle management. The ideal candidate brings strong experience with AWS EKS, Infrastructure-as-Code, CI/CD automation, and distributed systems, along with a passion for platform reliability, scalability, and operational excellence. Working closely with Data Scientists, ML Engineers, and Software Engineers, you will play a key role in modernizing infrastructure, improving developer productivity, and advancing AI capabilities across the organization.

Key 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 Infrastructure-as-Code 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 solutions across distributed systems and GPU clusters.
• Troubleshoot complex performance, scalability, and reliability issues across ML platforms and infrastructure.

Required Qualifications

• 5+ years of experience in DevOps, SRE, Platform Engineering, or Infrastructure Engineering roles.
• 3+ years supporting production AI/ML or Machine Learning platforms.
• Strong hands‑on experience with Kubernetes and workload orchestration.
• Experience with Kubernetes batch schedulers such as Volcano and/or Kueue.
• Experience managing production workloads on AWS EKS.
• Strong programming skills in Python and/or Golang.
• Experience with Terraform, Helm, and Infrastructure-as-Code methodologies.
• Experience building CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI/CD, or Azure DevOps.
• Strong Linux administration and troubleshooting capabilities.
• Experience implementing monitoring and observability solutions using Prometheus and Grafana.
• Solid understanding of distributed systems, containerization, cloud‑native architecture, and microservices.
• Knowledge of ML lifecycle management, model deployment, and production operations.

Preferred Qualifications

• Experience supporting GPU‑intensive AI/ML environments.
• Hands‑on experience with Kubeflow and/or MLflow.
• Experience with distributed training frameworks and GPU resource management.
• Familiarity with LLMOps, RAG architectures, Generative AI, and vector databases.
• Experience with ArgoCD or Flux.
• Experience supporting multi‑cluster Kubernetes environments.

Role Overview

We are seeking a DevOps Engineer with deep expertise in cloud-native infrastructure, Kubernetes, and MLOps to build and support enterprise-scale machine learning platforms. This role is responsible for designing, automating, and optimizing the infrastructure that powers AI/ML workloads, enabling efficient model training, deployment, monitoring, and lifecycle management. The ideal candidate brings strong experience with AWS EKS, Infrastructure-as-Code, CI/CD automation, and distributed systems, along with a passion for platform reliability, scalability, and operational excellence. Working closely with Data Scientists, ML Engineers, and Software Engineers, you will play a key role in modernizing infrastructure, improving developer productivity, and advancing AI capabilities across the organization.

Key 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 Infrastructure-as-Code 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 solutions across distributed systems and GPU clusters.
• Troubleshoot complex performance, scalability, and reliability issues across ML platforms and infrastructure.

Required Qualifications

• 5+ years of experience in DevOps, SRE, Platform Engineering, or Infrastructure Engineering roles.
• 3+ years supporting production AI/ML or Machine Learning platforms.
• Strong hands‑on experience with Kubernetes and workload orchestration.
• Experience with Kubernetes batch schedulers such as Volcano and/or Kueue.
• Experience managing production workloads on AWS EKS.
• Strong programming skills in Python and/or Golang.
• Experience with Terraform, Helm, and Infrastructure-as-Code methodologies.
• Experience building CI/CD pipelines using GitHub Actions, Jenkins, GitLab CI/CD, or Azure DevOps.
• Strong Linux administration and troubleshooting capabilities.
• Experience implementing monitoring and observability solutions using Prometheus and Grafana.
• Solid understanding of distributed systems, containerization, cloud‑native architecture, and microservices.
• Knowledge of ML lifecycle management, model deployment, and production operations.

Required Technical Skills

Kubernetes, AWS EKS, Terraform, Helm, Python, Golang, CI/CD, GitHub Actions, Jenkins, GitLab CI/CD, Azure DevOps, Linux, Prometheus, Grafana, Kubeflow, MLflow, Distributed Systems, Cloud‑Native Architecture, Containerization, Microservices

Preferred Qualifications

• Experience supporting GPU‑intensive AI/ML environments.
• Hands‑on experience with Kubeflow and/or MLflow.
• Experience with distributed training frameworks and GPU resource management.
• Familiarity with LLMOps, RAG architectures, Generative AI, and vector databases.
• Experience with ArgoCD or Flux.
• Experience supporting multi‑cluster Kubernetes environments.

The pay range that the employer in good faith reasonably expects to pay for this position is $34.88/hour - $54.50/hour. Our offered benefits include medical, dental, vision and retirement benefits. Applications will be accepted on an ongoing basis. Tundra Technical Solutions is among North America’s leading providers of Staffing and Consulting Services. Our success and our clients’ success are built on a foundation of service excellence. We are an equal opportunity employer, and we do not discriminate on the basis of race, religion, color, national origin, sex, sexual orientation, age, veteran status, disability, genetic information, or other applicable legally protected characteristic. Qualified applicants with arrest or conviction records will be considered for employment in accordance with applicable law, including the Los Angeles County Fair Chance Ordinance for Employers and the California Fair Chance Act. Unincorporated LA County workers: we reasonably believe that criminal history may have a direct, adverse and negative relationship with the following job duties, potentially resulting in the withdrawal of a conditional offer of employment: client provided property, including hardware (both of which may include data) entrusted to you from theft, loss or damage; return all portable client computer hardware in your possession (including the data contained therein) upon completion of the assignment, and; maintain the confidentiality of client proprietary, confidential, or non-public information. In addition, job duties require access to secure and protected client information technology systems and related data security obligations.