Your role

In this role, you will support us to ensure robust, stable, trustworthy, and reliable Machine Learning (ML) solutions. You will address the challenges of detecting data and concept drifts in our models and mitigate them. You will be responsible for monitoring and maintaining the infrastructure and software to enable ML 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. Together with the team, you will implement best practices to improve the existing ML infrastructure focusing on scalability and stability.

Your profile

• An excellent university degree (Ph.D. 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 with at least one automation solution (e.g. Azure DevOps, Jenkins, Ansible, Terraform)
• Knowledge of 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

Your ZEISS Recruiting Team:

Annett Burger