Contractor Machine Learning Engineer - Large Language Models

• Adapt LLMs to diverse healthcare use-cases using techniques such as Sparse Fine-Tuning (SFT), Prompt Engineering Fine-Tuning (PEFT), Direct Parameter Optimization (DPO), and Proximal Policy Optimization (PPO).
• Optimize LLMs for Retriever-Augmented Generation (RAG) to enhance decision-making and information retrieval capabilities.
• Collect, clean, and refine healthcare datasets for training LLMs to ensure high-quality data provisioning.
• Convert models into various formats suitable for production environments, ensuring their readiness for real-world application.

• 5+ years of hands-on professional experience in software engineering, building production-grade deep learning solutions.
• An academic degree in computer science, data science, or a related degree. M.Sc. or Ph.D. degree is strongly preferred.
• Demonstrated expertise in model tuning frameworks like Axolotl.
• Familiarity with model serving frameworks, including vLLM, TGI, and llama-cpp, to support the deployment and scalability of machine learning models.
• Knowledge of model quantization techniques and frameworks to optimize AI models for performance in resource-constrained environments.
• Hands-on experience with Transformer architectures and proficiency in machine learning frameworks such as PyTorch.

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