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Mid-level generalist MLOps/Data role in Bangalore with 3–6 years attracts high applicant density.
Core MLOps and data engineering skills are transferable, but clinical/PHI experience narrows fit.
Explicit 3–6 years plus mandatory Python, CI/CD, cloud, and data pipeline skills increases strictness.
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Build and maintain data pipelines and infrastructure for clinical document processing and ML workflows including training, inference, evaluation, and experiment tracking.
Support deployment cycles and collaborate with Engineering DevOps to ensure secure, reproducible, and production-friendly ML system operations.
Develop internal tools and automate ML workflows to reduce manual operational burden and improve reliability and reproducibility.
3-6+ years of experience in data engineering, MLOps, backend engineering for ML systems, or related production data workflows.
Strong Python programming skills and experience with Docker, Git, CI/CD, APIs, cloud infrastructure, and production monitoring.
Experience in building and maintaining data pipelines, workflow orchestration, object storage, databases, and batch/stream processing.
Comfortable with ML workflows such as experiment tracking, model versioning, inference deployment, and evaluation pipelines.
Experienced in end-to-end ML infrastructure ownership including data pipeline construction and deployment, working with Research Engineers and ML Evaluation Engineers.
Demonstrates strong engineering discipline in logging, testing, documentation, reproducibility, security, and maintaining production-grade ML systems.
Able to quickly build practical internal tools (e.g., using Streamlit) that support annotation, evaluation, and data inspection workflows.