Back
Tier-1 employer and Bangalore location increase competition, but senior specialized MLOps focus limits applicant density.
Strong ML engineering and regulated clinical/GxP experience required, limiting cross-industry transferability.
Explicit 9–10 years plus mandatory Azure Databricks, MLflow, MLOps and regulated/GxP experience.
Login to See Your Match Score
Create a free account or log in to unlock your CV match score across:
Design, build and deploy production-grade machine learning and GenAI/LLM solutions for Clinical Operations using Azure Databricks, MLflow, and Azure Machine Learning.
Own the end-to-end ML lifecycle including data ingestion, feature engineering, model serving, monitoring and MLOps automation in a regulated environment.
Collaborate cross-functionally to translate data science experiments into scalable production code and maintain MLOps best practices with compliance to GxP and data governance.
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 with enterprise-scale production deployment and operation of ML models.
Proficiency with Azure Databricks (Spark, Delta Lake, Feature Store), MLflow (tracking, model registry, serving), Python programming, ML frameworks (Scikit-learn, PyTorch, TensorFlow).
Experience with API development (FastAPI, Flask), CI/CD pipelines (Azure DevOps or GitHub Actions), Docker containerization, and Azure cloud services (ADLS Gen2, Azure Key Vault).
Experienced in architecting and operating scalable, maintainable ML systems in regulated, enterprise environments focusing on Clinical Operations use cases.
Strong practitioner of software engineering best practices and MLOps automation working in agile, cross-functional teams with data scientists and engineers.
Comfortable communicating complex technical concepts clearly to non-technical stakeholders and mentoring junior engineers in cloud-native ML engineering on Azure platforms.