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Mid-level popular ML role, metro Bangalore location, and broad MLOps/cloud skill requirements increase competition.
MLOps skills are transferable but healthcare compliance and regulated-data experience increase domain specificity.
Explicit 3–5 year requirement, mandatory production ML experience, and specific tech/compliance needs make screening strict.
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Build and maintain CI/CD pipelines for ML including automated testing, model deployment, and monitoring for healthcare AI systems.
Deploy scalable ML models as APIs/microservices ensuring performance and latency suitable for clinical use.
Develop and optimize ETL processes and integrate ML outputs with healthcare applications, ensuring system reliability and compliance with HIPAA/HITRUST.
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or related field.
3–5 years professional experience in software or data engineering, with at least 2 years in machine learning production environments.
Strong proficiency in Python and familiarity with SQL; experience with cloud platforms (AWS, Azure, or GCP) and containerization (Docker).
Work location: Bangalore (Hybrid); Travel up to 10% domestic.
Experienced in MLOps with strong focus on pipeline automation, deployment, and production stability in healthcare or regulated environments.
Skilled in data engineering including healthcare data standards (FHIR, HL7) and building feature stores consistent across training and production.
Proficient in cloud infrastructure management, container orchestration (Kubernetes), and adherence to security/compliance protocols.