DevOps Engineer

• Role: Platform DevOps-Engineer
• Location: Munich, Germany
• Job Type: Contract - 2 Days Onsite
• Note: No Sponsorship Provided

Main Duties and Responsibilities

• You’ll shape our cloud-native architecture across AWS and Kubernetes, driving best-in-class infrastructure-as-code, CI/CD, and automation to accelerate model development and deployment.
• You’ll design and operate Kubeflow (or similar) pipelines and supporting services, streamline developer workflows, and raise the reliability and efficiency of the platform.
• Day to day, you’ll partner with product engineering, data scientists, and imaging teams to translate scientific and product needs into secure, production-grade infrastructure that scales.
• Design, deploy, and maintain Kubeflow (or equivalent) for pipeline orchestration, model training, evaluation, and serving on large image datasets; ensure reliability, security, and cost efficiency.
• Manage and tune Kubernetes clusters (EKS/GKE/AKS), set up namespaces, RBAC, autoscaling, network policies, and service meshes where appropriate; keep upgrades and operations predictable.
• Define infrastructure-as-code with Terraform; implement repeatable environment provisioning, configuration management, and golden paths for teams.
• Establish CI/CD workflows (GitHub Actions/Jenkins/GitLab CI), build/test standards, and progressive delivery patterns that keep releases fast and low-risk.
• Implement logging, metrics, and tracing (e.g., Prometheus, Grafana, CloudWatch, Splunk/New Relic) with actionable SLOs, alerts, and runbooks; embed security and compliance by design.
• Collaborate closely with product and science teams to remove bottlenecks, eliminate manual steps, and evolve service and data interfaces that make operating image pipelines simple and reliable.
• Contribute to future-state architectures that improve scalability, resiliency, and operational efficiency; lead targeted refactors and platform improvements.
• Manage core automation and tooling, and educate teams on platform capabilities, CI/CD, configuration management, and infrastructure automation best practices.

Required (Must-have)

• M.Sc. in Computer Science/Engineering (or equivalent) or comparable industry experience.
• Practical, production experience operating Kubeflow Pipelines for reproducible ML workflows at scale.
• Proven experience deploying and operating workloads on Kubernetes (EKS/GKE/AKS), including upgrades, autoscaling, RBAC, networking, and reliability; strong Unix/Linux fundamentals.
• Hands-on experience with AWS services (EKS, EC2, S3, IAM, CloudWatch; RDS a plus) and the ability to design secure, cost-aware architectures.
• Strong Terraform skills and Git-based workflows for repeatable infrastructure provisioning and configuration management.
• Practical experience with CI/CD platforms (GitHub Actions/Jenkins/GitLab CI), including artifact management, environment promotion, and progressive delivery.
• Solid Python and/or shell scripting for platform automation and toil reduction.
• Experience implementing logging, metrics, and tracing with SLOs, alerts, and runbooks (e.g., Prometheus, Grafana, CloudWatch, Splunk/New Relic) and a security-first mindset.
• Ability to lead technical initiatives, communicate trade-offs clearly, and collaborate effectively with engineering and science teams

Desirable (Nice to have)

• Experience with ML flow, Feast, Argo, Airflow, Ray, and model versioning/monitoring.
• Familiarity with S3/object storage, artifact registries, and handling large image datasets; basic SQL/NoSQL exposure.
• Experience with digital pathology or large-scale image processing (e.g., whole-slide images) and tools like Open Slide, scikit-image, or OpenCV.
• Experience tuning high-throughput pipelines, concurrency, memory usage, and integrating GPUs/accelerators.
• Experience with VPC design, ingress/egress, service meshes, secrets management, IAM, and policy as code.
• Experience in regulated environments (e.g., GxP), including data governance, privacy, and building software under regulated processes.
• Experience with Jira/Zendesk and with JavaScript/TypeScript for internal tools or dashboards.