Machine Learning Engineer (MLOps & AI Infrastructure)

At Roche you can show up as yourself, embraced for the unique qualities you bring. Our culture encourages personal expression, open dialogue, and genuine connections, where you are valued, accepted and respected for

, allowing you to thrive both personally and professionally. This is how we aim to prevent, stop and cure diseases and ensure everyone has access to healthcare today and for generations to come. Join Roche, where every voice matters. The Position Machine Learning Engineer (MLOps & AI Infrastructure) Roche India – Roche Services & Solutions Hyderabad / Chennai A healthier future. It’s what drives us to innovate. To continuously advance science and ensure everyone has access to the healthcare they need today and for generations to come. Creating a world where we all have more time with the people we love. That’s what makes us Roche. Roche has established the Global Analytics and Technology Center of Excellence (GATE) to drive analytics- and technology-driven solutions by partnering with Roche affiliates across the globe. GATE enables data-led decision-making and innovation across healthcare and biotech operations. To learn more

: visit As a Machine Learning Engineer (MLOps), you will play a critical role in designing, building, and maintaining scalable machine learning systems within Roche’s data ecosystem. You will collaborate closely with data scientists, data engineers, and business stakeholders to develop production-grade ML infrastructure that supports real-world healthcare and commercial applications. This position demands a blend of technical expertise, problem-solving ability, and strong ownership of MLOps processes to ensure that Roche’s ML models are production-ready, monitored, and continuously improving. Your Opportunity: ML Infrastructure and Pipeline Development (Primary Focus): • Design, build, and maintain scalable production-grade ML pipelines for data ingestion, model training, and inference • 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 lifecycles Model 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 systems Collaboration and Project Ownership (Primary Focus): • Partner with Stakeholders, data scientists, product managers, and IT teams to translate business