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In this role, you will be responsible for monitoring and maintaining the infrastructure and software to enable ML models and advanced analytics to act, as well as develop solutions for detection and mitigation of data and concept drifts in our models. You will work together with our Data Engineering, Data Science and ML Engineering teams. In your work, you will apply your deep end-to-end understanding of applications including ML models to ensure a reliable and stable operation. Together with the team you will implement best practices to improve the existing ML infrastructure focusing on scalability and stability.

An excellent university degree (PhD is a plus) in natural science, mathematics, computer science, engineering or similar

At least 2+ years of professional experience in a DevOps, ML engineering, or Data Science role (MLOps, DevOps)

Experience in building up machine learning monitoring infrastructure including model and infrastructure monitoring

Good understanding of data science algorithms

First experience or strong interest in data drift & concept drift detection and algorithmic stability monitoring

Proven experience of at least one automation solution (e.g. Azure DevOps, Jenkins, Ansible, Terraform)

Knowledge in scripting (e.g. Bash, Powershell, Python)

REST APIs and state-of-the-art cloud technologies

Experience with virtualization technologies, Linux Container and/or K8s and Open Source are a plus

Strong communication skills

Fluent English and advanced German language skills\n \n \n \n \n \n

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