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Tier-1 brand, Bangalore location, mid-level ML title, and generalist MLOps requirements increase competition.
MLOps and data infrastructure skills are transferable, but healthcare compliance (FHIR, HIPAA) raises domain specificity.
Explicit 3–5 years plus 2 years ML production, cloud, HIPAA/HITRUST and specific tooling make filters strict.
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Build and maintain CI/CD pipelines and automated workflows for machine learning models powering Healthcare Information Systems (HIS).
Deploy ML models as scalable APIs/microservices ensuring performance and latency requirements for clinical use.
Develop and optimize ETL pipelines transforming healthcare data (FHIR, HL7) for model training and inference, integrating ML outputs into healthcare applications.
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 providers (AWS, Azure, or GCP) and Docker containerization.
Knowledge of ML tools (PyTorch or Scikit-learn), MLOps tools (Airflow, Prefect, BentoML, Kubeflow), and data processing frameworks (Pandas, Spark, or dbt).
Experienced in MLOps, focused on data reliability, production stability, and bridging data science with software engineering.
Skilled at developing scalable, secure ML services meeting regulatory standards (HIPAA, HITRUST) in healthcare environments.
Comfortable working with cloud infrastructure and container orchestration tools in a hybrid Bangalore work setting.