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Roche
Machine Learning Engineer (MLOps & AI Infrastructure)
Roche
Hyderabad•On-site•4-7 Yrs•2 hours ago
Job description
AI InfrastructureMLOpsProduction ML SystemsHealthcare AnalyticsData ScienceCloud Computing
Overview
Machine Learning Engineer (MLOps & AI Infrastructure) at Roche, designing and building scalable machine learning systems within Roche’s data ecosystem. Collaborates with data scientists, data engineers, and business stakeholders to develop production-grade ML infrastructure.
Responsibilities
- 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
- 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
- Partner with stakeholders 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
- Translate complex data insights into clear and actionable business strategies
- Promote best practices in coding, data handling, and project management
- Ensure adherence to Roche’s ethical AI standards and data privacy regulations
- 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
Qualifications
- Bachelor’s or master’s degree in Computer Science, Data Science, Machine Learning, or related fields
- 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
- 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
Nice 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
Education
- Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or related fields