- Develop and deploy Large language and machine learning models end-to-end.
- Clean, transform, and prepare large, complex healthcare datasets for machine learning model development, including handling missing values, outlier detection, feature engineering, and data normalization.
- Identify, collect, and curate relevant, industry-specific datasets for model retraining and format data appropriately for LLM and training pipeline.
- Design, train, and fine-tune various LLMs on extensive healthcare data to solve specific clinical or operational problems.
- Set up and manage the training environment, including GPU instances and required software.
- Experiment with and fine-tune hyperparameters such as learning rate, batch size, and training epochs to optimize model performance.
- Integrate structured and unstructured data (multi-modal/multi-input models).
- Evaluate model performance using appropriate metrics, identify areas for improvement, and implement optimization strategies.
- Develop and maintain robust and scalable data and ML pipelines for model training, inference, and deployment.
- Work closely with data scientists, clinicians, and software engineers to understand requirements, integrate models into production systems, and ensure data privacy and security compliance.
- Stay up-to-date with the latest advancements in machine learning and healthcare AI, and explore new technologies and methodologies.
- Maintain clear and comprehensive documentation of models, data pipelines, and experimental results.
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