Job description

As a DevOps Engineer (m/f/d) at Breuninger in the IT Operations department, you will help us shape the future of our IT infrastructure – between data center and cloud, between operations and development. In a highly diverse technological environment, you will be responsible for automation, platform stability, and the secure operation of modern applications. This also includes establishing a sound technical foundation for our internal use of AI .

Your new team – this is us: We work container-first and Infrastructure as Code, using Kubernetes, Terraform, and Ansible, GitLab as our CI/CD environment, Prometheus for observability, and Linux as our foundation. We also utilize virtualization (KubeVirt, VMware), storage (NetApp, Ceph), and identity services with Keycloak and Active Directory. Where we develop in-house, Go is our language of choice. Open source is in our DNA – not just a label, but a way of working.

These are the team's tasks. No one covers them all alone – you bring your expertise and grow into the other areas:

• Develop and operate infrastructure components, platform services and automation solutions
• Designing CI/CD environments and keeping deployment processes stable and secure
• Monitoring systems – monitoring, logging and alerting
• Operate internal AI tooling and build a solid AI Development Harness – from connecting to models and internal data sources to the question of how AI-supported work can be meaningfully evaluated and further developed.
• Kubernetes management, Infrastructure as Code, virtualization, storage, identity services and platform operation

Qualifications

• You think and work according to DevOps principles – operations and development are not opposites for you.
• Kubernetes and Git are a given for you; Linux is your primary working environment – ​​both on the server and on your notebook.
• For the rest of our stack, it's less about what you already know and more about your passion and willingness to learn.
• You write code to solve problems – ideally in Go, or with a willingness to learn it.
• You are interested in making AI tools usable in everyday work – initial experience with LLM APIs, agent workflows, MCP or evaluation of AI results is a plus.
• You work in a structured manner, automate recurring tasks, and have high quality standards for your work.