Key Responsibilities
- Lead the design, customization, and integration of large language models (LLMs) into biomedical research workflows and information retrieval systems
- Serve as a subject matter expert (SME) across product and engineering teams to define AI-enabled capabilities for NCBI platforms
- Develop and implement retrieval-augmented generation (RAG) systems integrating LLMs with large-scale biomedical datasets
- Provide architectural guidance on model selection, domain adaptation, evaluation strategies, and deployment approaches
- Improve model grounding, factual accuracy, and scientific reliability in domain-sensitive applications
- Support engineering teams in productionizing AI solutions with a focus on scalability, performance, and maintainability
Requirements
- 3+ years of hands-on experience with LLMs (training, fine-tuning, augmentation, or deployment)
- Proven experience integrating LLMs into production systems (e.g., semantic search, RAG pipelines, domain-specific QA)
- Strong expertise in ML system architecture and scalable deployment
- Proficiency in Python and modern ML frameworks (e.g., PyTorch, Hugging Face)
- Experience with retrieval infrastructure (e.g., embeddings, vector databases)