ML Ops & Data Engineer

Position: ML

Location: Bangalore

Working Days: Monday to Friday

Experience Required: 3-6+ years

GROWTH PATH

This is an individual contributor role with strong ownership expectations. High performers may be considered for workstream lead or functional lead responsibilities after approximately 12 months, based on demonstrated ownership, delivery, technical judgment, mentoring, cross-functional influence, and ability to reduce dependency on the Director of ML.

ABOUT THE ROLE

We are looking for an MLOps & Data Engineer to build the infrastructure that allows our ML team to process clinical documents, run experiments, deploy models, monitor systems, and support annotation/evaluation workflows.

You will work closely with Research Engineers, ML Evaluation Engineers, Clinical AI Data Specialists, Engineering DevOps, and backend teams. This role requires both ML infrastructure and practical data engineering skills.

WHAT YOU WILL DO

• Build and maintain data pipelines for clinical document processing, OCR outputs, text extraction, metadata normalization, and dataset preparation.
• Support deployment cycles for ML/LLM systems in collaboration with Engineering DevOps.
• Build and maintain training, inference, and evaluation infrastructure.
• Improve experiment tracking, model versioning, dataset versioning, CI/CD, monitoring, observability, and reproducibility.
• Build internal tools and lightweight Streamlit apps for annotation, clinical review, evaluation, QA, data inspection, and project operations.
• Automate recurring ML workflows and reduce manual operational burden on Research Engineers.
• Work with Research Engineers to productionize reliable prototypes.
• Work with ML Evaluation Engineers to support evaluation pipelines, hidden test set runs, regression automation, and production monitoring.
• Ensure systems are secure, reproducible, maintainable, and production-friendly.

WHAT WE EXPECT

• 3–6+ years of experience in data engineering, MLOps, backend engineering for ML systems, ML platform work, or production data workflows.
• Strong Python skills and comfort with data processing, APIs, scripts, and internal tools.
• Experience with Docker, Git, CI/CD, APIs, cloud infrastructure, and production monitoring.
• Experience with data pipelines, workflow orchestration, object storage, databases, and batch/stream processing.
• Familiarity with ML workflows such as experiment tracking, model registry, inference deployment, and evaluation pipelines.
• Ability to build practical internal tools quickly, including Streamlit or similar lightweight apps.
• Strong engineering discipline: logging, tests, reproducibility, documentation, reliability, and security-aware data handling.

NICE TO HAVE

• Experience with LLM serving, vLLM, Ray, Triton, Kubernetes, Terraform, Airflow, Prefect, MLflow, Weights & Biases, FastAPI, Streamlit, or similar tools.
• Experience with OCR/document pipelines, PDFs, TIFF/JPEG processing, EHR data, or healthcare data systems.
• Experience working with DevOps/SRE teams and understanding where ML platform ownership should sit versus engineering DevOps ownership.
• Familiarity with PHI/PII-aware data handling and secure data workflows.

SUCCESS IN 6 MONTHS

• Establishes reliable ML deployment and data-processing workflows.
• Reduces RE time spent on infra, manual data preparation, and ad hoc tooling.
• Builds useful internal tools for annotation, evaluation, review, and data inspection.
• Improves reproducibility of experiments and releases.
• Works effectively with Engineering DevOps without requiring the engineering team to own all ML-specific infra.