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.
Develop methods to evaluate and compare the performance of different models.
Automate processes for validating model accuracy and consistency in production.
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.
Create comprehensive documentation for processes, pipelines, and workflows.
Provide training and guidance to team members on MLOps best practices.

At least 2 years working experiences 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.

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