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Metro mid-level ML role with generalist MLOps skills and broad requirements increases applicant competition.
Healthcare-specific FHIR/HL7 and HIPAA requirements limit portability despite transferable MLOps skills.
Explicit 3–5 years plus 2 years ML production and mandatory cloud, Docker/Kubernetes, and HIPAA compliance.
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Build and maintain CI/CD pipelines for machine learning models with focus on automated testing, deployment, and version control.
Deploy and monitor scalable ML models as APIs/microservices within Healthcare Information Systems ensuring production stability and compliance with security standards.
Develop and optimize ETL pipelines for healthcare data (FHIR, HL7), integrate ML outputs into core healthcare applications, and uphold code quality and containerization best practices.
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, including 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) mandatory.
Work location: Bangalore - Hybrid; Work Experience Required: 3–5 years with at least 2 years in ML production environments.
Experienced in MLOps and productionizing machine learning models within regulated environments such as healthcare.
Comfortable bridging data science and software engineering by managing cloud infrastructure, automated workflows, and scalable microservices.
Proficient in ML tools (PyTorch, Scikit-learn), MLOps frameworks (Airflow, Prefect, Kubeflow), and data processing frameworks (Pandas, Spark), demonstrating operational focus on production stability and security compliance.