ML / MLOps Engineer

Responsibilities:

• Build and maintain data pipelines for clinical document processing, OCR outputs, text extraction, metadata normalisation, and dataset preparation.
• Support deployment cycles for ML/LLM systems in collaboration with Engineering and 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.

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.
• Strong engineering discipline: logging, tests, reproducibility, documentation, reliability, and security-aware data handling.