Responsibilities

• Implement automated workflows for data preprocessing, feature engineering, and model retraining
• Collaborate with data scientists to operationalize ML models and ensure smooth transition from experimentation to production
• Develop reusable frameworks and internal tools to standardize and accelerate ML development lifecyclesModel Deployment and Monitoring (Primary Focus): Deploy and manage ML models in production environments using cloud-based services (AWS preferred)Implement monitoring frameworks for data drift, model drift, and performance degradation
• Maintain high availability, reliability, and scalability of deployed models through robust engineering practices
• Develop alerting systems to ensure timely remediation and maintenance of production ML systemsCollaboration and Project Ownership (Primary Focus):Partner with Stakeholders, data scientists, product managers, and IT teams to translate business requirements into scalable ML architectures
• Promote best practices in coding, data handling, and project management within the data science team, ensuring high-quality deliverables
• Ensure adherence to Roches ethical AI standards and data privacy regulationsGenAI, Automation, and Emerging Technologies (Secondary / Emerging Focus): Collaborate with AI research teams to integrate Generative AI solutions into ML workflows and pipelinesExperiment with LLMs and prompt-based workflows to enhance automation and model explainability