To support our growing team, we are looking for an experienced DevOps Engineer to join us. In this role, you will take both a strategic and hands-on approach to designing, building, and scaling our infrastructure. You will play a key role in optimizing performance, enabling secure access for both internal teams and customers, and continuously improving our systems in a cutting-edge AI infrastructure company.
This position is open in a remote or hybrid capacity for candidates currently based in Germany.
What you’ll
- doDesign, build, and maintain scalable, production-grade infrastructure across cloud and on-premise environmen
- tsSet up and manage Kubernetes clusters, ensuring reliability, scalability, and performan
- ceBuild and optimize robust CI/CD pipelines (e.g. GitHub Actions, GitLab CI) to support fast and secure deploymen
- tsAutomate infrastructure provisioning using tools like Terraform and Dock
- erEnsure high availability, performance, and observability across all syste
- msImplement secure access solutions for internal teams and customer environments (including on-prem deployment
- s)Apply security best practices to protect infrastructure, pipelines, and intellectual proper
- tyCollaborate closely with engineering and product teams to continuously improve systems and process
- esDrive improvements, introduce new technologies, and contribute to the DevOps roadm
ap
What we’re looking
- forStrong hands-on experience designing and operating Kubernetes clusters (cloud and/or on-premi
- se)Solid background working with at least one major cloud provider (preferably AWS; Azure/GCP also welco
- me)Deep understanding of Linux systems and networking fundament
- alsProven experience building and maintaining CI/CD pipelines in production environme
- ntsExperience with Infrastructure as Code (e.g. Terrafo
- rm)Familiarity with observability and monitoring tools (e.g. Prometheus, Grafana, Datad
- og)Ability to work independently, take ownership, and operate effectively with minimal onboard
ing
Nice to
- haveExperience with DevSecOps or MLOps pract
- icesExposure to AI/ML infrastructure, data pipelines, or frameworks like TensorFlow or PyT
- orchExperience with OpenStack or multi-cloud environm
ents
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