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Mid-level ML title, metro Bangalore location, and broad MLOps skills increase candidate competition.
Healthcare-specific data standards and compliance needs make cross-industry transferability limited.
Explicit 3–5 years requirement plus mandatory MLOps, cloud, and regulated healthcare experience enforces strict filters.
Build and maintain CI/CD pipelines for machine learning model deployment ensuring automated testing, version control, and scalable API/microservice serving in healthcare environments.
Develop and optimize ETL and feature management pipelines to transform healthcare data (FHIR, HL7) for model training and inference, integrating ML outputs with healthcare applications.
Ensure production stability, data reliability, system monitoring, and compliance with HIPAA and HITRUST security standards in ML workloads using containerization and cloud infrastructure.
Bachelor’s or Master’s degree in Computer Science, Software Engineering, Data Engineering, or related fields.
3–5 years of professional software or data engineering experience, including 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 containerization (Docker).
Experience working in healthcare data environments and following HIPAA/HITRUST compliance standards.
Experienced engineer focused on MLOps with practical skills bridging data science and software engineering for production-grade ML systems.
Proficient in building scalable, reliable ML pipelines and services in regulated healthcare environments.
Demonstrates expertise in cloud-based deployments, container orchestration (Kubernetes), and monitoring for AI systems supporting clinical use cases.