We are tackling a unique challenge, together with industry and research, building a MLOps platform that will scale across 1000s of nodes covering the ML Lifecycle end-to-end. The ML platform is currently deployed and used in production at multiple large customers.In the MLOps focused team we are responsible for: gathering, understanding and synthesizing MLOps requirements of our customers designing and presenting MLOps solutions to address those, including architectures and best practices deploying, maintaining and extending the functionality of ML platforms such as our own deploying and maintaining Data Science use cases/ ML models on those platforms establishing a presence in the MLOps community, through meetups, conferences, hackathons For that we are looking for a candidate that is interested joining our team who is self-driven, committed and with a strong customer focus.

Minimum 2 years of professional experience as a DevOps or Data Engineer having built and maintained critical infrastructure/data solutions in production Experience with container orchestration, e.g. Kubernetes and its toolset, e.g. Helm Experience with cloud stacks, e.g. AWS or Azure and Infrastructure as Code e.g. Terraform Experience with CI/CD, monitoring and alerting, incident management Experience with workflow management tools e.g. Airflow Experience working with common concepts and tools in the MLOps Landscape (applications like Kubeflow, MLFlow) or commitment to learn Knowledge of the Machine Learning lifecycle from explorative data analysis, model training to model serving Very good Python and/or Go skills Strong verbal and written communication skills in English, German is a nice-to-have You set realistic deadlines and stick with them, you are highly organized and understand how to prioritize