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Job Description
Structured overview of role & requirementsAbout This Role
Design, build and operationalize production-grade machine learning and GenAI/LLM solutions on Azure Databricks for Global Clinical Operations business problems such as trial feasibility, site selection, and patient recruitment forecasting.
Own the end-to-end ML lifecycle including data ingestion, feature engineering, versioning, serving, monitoring and governance in a regulated environment with MLOps best practices.
Collaborate across cross-functional agile teams and mentor junior engineers to deploy scalable, maintainable, and compliant AI/ML systems integrated with Azure cloud services.
Minimum Requirements
Bachelor’s or master’s degree in Computer Science, Software Engineering, Mathematics, Statistics or related quantitative field.
9-10 years of hands-on ML engineering or MLOps experience deploying and operating ML models in enterprise production environments.
Strong expertise in Azure Databricks ecosystem including Spark, Delta Lake, Unity Catalog, MLflow, and Azure Machine Learning.
Solid skills in Python programming, ML frameworks (Scikit-learn, PyTorch, TensorFlow), API development (FastAPI, Flask), CI/CD workflows, containerization (Docker), and Azure cloud services.
Ideal Candidate Profile
Experienced engineer with a strategic focus on delivering robust, scalable AI/ML solutions at enterprise scale within regulated (GxP) domains like Clinical Operations.
Practitioner who excels at bridging experimental data science with software engineering best practices for productionizing models in cloud-native environments.
Effective communicator capable of managing technical and non-technical stakeholders while ensuring compliance, data governance and responsible AI principles.