Your Role in More Detail:

MLOps Pipeline Development and Optimization:

• Design and maintain scalable pipelines for deploying machine learning models whether in-cloud or in-vehicle.
• Ensure models are securely integrated into production environments with minimal latency.
• Implement monitoring systems to track model performance and flag issues.

Model Comparison and Validation:

• Develop methods to evaluate and compare the performance of different models.
• Automate processes for validating model accuracy and consistency in production.

Collaboration:

• Work closely with data scientists, developers, and stakeholders to understand their needs and provide tailored solutions.
• Effectively communicate technical processes and outcomes to both technical and non-technical audiences.

Documentation and Knowledge Sharing:

• Create comprehensive documentation for processes, pipelines, and workflows.
• Provide training and guidance to team members on MLOps best practices.

Your profile

• Proficient in modern DevOps practices and microservice architecture.
• Expertise in Kubernetes and containerization technologies.
• Hands-on experience with platforms such as KubeFlow, Kserve, or equivalent.
• Experience in ML Experimentation and registry platforms such as W&B or MLFLow.
• Understanding of time series modeling and its data requirements.
• Familiar with ML/NN frameworks.
• Familiar with AWS or other cloud service providers is a plus.
• Strong ability to collaborate with cross-functional teams, including data scientists, engineers, and clients.
• Clear and concise in verbal and written communication, with excellent documentation skills.
• Fluent in both written and spoken English. German is a plus.