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# DevOps Engineer(AI)

- **Company:** Capgemini
- **Location:** Brussel, Belgium
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Microsoft Azure, Bash Shell, Cloud Computing, Cloud Engineering, Continuous Integration, DevOps, Github, Monitoring of Systems, Python (Programming Language), Machine Learning, Networking Basics, Windows PowerShell, Reliability Engineering, Ansible, Azure Machine Learning, Software Engineering, Management of Software Versions, Scripting, Google Cloud, Cloudformation, Containerization, Gitlab-ci, Kubernetes, Infrastructure Automation Frameworks, Deployment Automation, Machine Learning Operations, Virtual Agents, Terraform, Docker, Jenkins
- **Published:** September 17, 2026
- **Apply:** https://www.adzuna.be/details/5886258717

## About the Role

* 4+ years of experience in DevOps, Cloud Engineering, or Site Reliability Engineering.
 * Strong experience with Azure, AWS, or GCP.
 * Hands-on expertise in Docker, Kubernetes, Jenkins, GitLab CI/CD, GitHub Actions, or Azure DevOps.
 * Experience with Infrastructure as Code tools such as Terraform or Ansible.
 * Knowledge of Linux administration, scripting (Python, Bash, PowerShell), and networking fundamentals.
 * Experience deploying and managing AI/ML workloads and MLOps platforms.

## Description

We are seeking a highly skilled DevOps Engineer with AI/ML platform experience to design, implement, and maintain scalable cloud infrastructure supporting AI-driven applications. The ideal candidate will have expertise in DevOps automation, CI/CD, cloud technologies, containerization, and MLOps practices to enable efficient deployment and monitoring of AI solutions., * Design, implement, and maintain CI/CD pipelines for cloud-native and AI applications.

 * Manage and optimize infrastructure on AWS, Azure, or Google Cloud Platform.
 * Automate deployment, monitoring, and scaling using Infrastructure as Code (Terraform, Ansible, CloudFormation).
 * Support AI/ML model deployment, versioning, and lifecycle management using MLOps practices.
 * Implement containerization and orchestration using Docker and Kubernetes.
 * Monitor system performance, reliability, security, and cost optimization.
 * Collaborate with Data Scientists, AI Engineers, and Software Development teams to operationalize AI solutions.


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