MLOps and Data Engineer

We are looking for an MLOps and 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.

Responsibilities:

• 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 the 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.

Requirements:

• 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.