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 requirements into scalable ML architectures Take end-to-end ownership of MLOps initiatives, from design through deployment and continuous monitoring Champion engineering excellence by enforcing best practices in CI/CD, version control, and automated testing Contribute to Roche’s broader AI/ML roadmap by developing infrastructure that supports both traditional ML and emerging GenAI applications Communication, Mentorship, and Governance (Primary Focus): Translate complex data insights into clear and actionable business strategies that address stakeholder needs and expectations Promote best practices in coding, data handling, and project management within the data science team, ensuring high-quality deliverables Ensure adherence to Roche’s ethical AI standards and data privacy regulations GenAI, Automation, and Emerging Technologies (Secondary / Emerging Focus): Collaborate with AI research teams to integrate Generative AI solutions into ML workflows and pipelines Experiment with LLMs and prompt-based workflows to enhance automation and model explainability Support the adoption of workflow orchestration tools such as Kubeflow, Airflow, or MLFlow for model lifecycle management Who you are: You are someone with bachelor’s or master’s degree in Computer Science, Data Science, Machine Learning, or related fields and 4+ years of professional experience in Machine Learning Engineering, Data Engineering, or MLOps roles Certifications in MLOps, AWS Cloud, or Data Engineering are highly desirable Proven experience building and deploying ML systems at scale in production, with strong understanding of supervised, unsupervised, and NLP models Hands-on experience with large-scale data processing using distributed computing frameworks Strong analytical, problem-solving, and debugging skills with attention to scalability and reliability Demonstrated ability to work independently and take ownership of end-to-end ML systems Proficiency in Python, PySpark, and SQL for data engineering and ML workflows Experience with scikit-learn, Spark MLlib, TensorFlow, PyTorch, and MLflow Extensive hands-on experience with AWS services such as S3, SageMaker, Glue, Lambda, Athena, EMR, and SageMaker Pipelines. Familiarity with GCP or Azure ML environments is a plus Expertise in version control (Git/GitHub), CI/CD (GitHub Actions, Jenkins), and model registry workflows Experience with Docker and Kubernetes for containerization and orchestration Proven track record of building and releasing ML frameworks or internal tools to accelerate model deployment Basic understanding of pharmaceutical datasets (e.g., IQVIA, SHA, Patients data) and familiarity with US healthcare markets would be a plus Strong analytical and problem-solving skills with a data-driven mindset Good to Have: Experience with Kubeflow, Airflow, or Prefect for ML pipeline orchestration Exposure to Generative AI (LLMs, transformers) and integration of GenAI into enterprise ML workflows Familiarity with data governance, security, and ethical AI practices in production environments

About Roche

Roche is a global pioneer in pharmaceuticals and diagnostics focused on advancing science to improve people’s lives. The combined strengths of pharmaceuticals and diagnostics under one roof have made Roche the leader in personalised healthcare – a strategy that aims to fit the right treatment to each patient in the best way possible. Roche is the world’s largest biotech company, with truly differentiated medicines in oncology, immunology, infectious diseases, ophthalmology and diseases of the central nervous system. Roche is also the world leader in in vitro diagnostics and tissue-based cancer diagnostics, and a frontrunner in diabetes management. Founded in 1896, Roche continues to search for better ways to prevent, diagnose and treat diseases and make a sustainable contribution to society. The company also aims to improve patient access to medical innovations by working with all relevant stakeholders. Thirty medicines developed by Roche are included in the World Health Organization Model Lists of Essential Medicines, among them life-saving antibiotics, antimalarials and cancer medicines. Roche has been recognised as the Group Leader in sustainability within the Pharmaceuticals, Biotechnology & Life Sciences Industry ten years in a row by the Dow Jones Sustainability Indices (DJSI).