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