Key Responsibilities

Serve as the AI/LLM subject matter expert across product and engineering teams.
Collaborate with product managers and technical leads to define AI-enabled capabilities and define the use of LLMs across NCBI platforms (e.g., PubMed and related systems).
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, ensuring scalability, performance, and maintainability.
Evaluate emerging LLM techniques and recommend practical adoption strategies aligned with organizational priorities.

Required Qualifications

3+ years of hands-on experience working with large language models (training, fine-tuning, augmentation, or deployment).
Demonstrated experience integrating LLMs into production systems (e.g., semantic search, RAG pipelines, domain-specific QA).
Strong experience in ML system architecture and scalable deployment.
Proven ability to work cross-functionally with product and technical teams.
Experience serving as a technical SME guiding multi-team initiatives.
Strong programming skills in Python.
Experience with modern ML frameworks (e.g., PyTorch, Hugging Face) and retrieval infrastructure (e.g., embeddings, vector databases).

Experience building LLM-based systems for biomedical research or life sciences.
Familiarity with large scientific corpora, biomedical ontologies, structured knowledge bases, or biological datasets.
Experience developing generative AI systems for DNA, RNA, or protein sequence analysis.
Background in bioinformatics, computational biology, or related disciplines.
Experience improving factual grounding and reducing hallucinations in scientific or regulated environments.