Senior AI and Large Language Model (LLM) Engineer

<p><strong>LOCATION: Bethesda, Maryland <span style="text-decoration: underline;">(On-site / Not-remote)</span></strong></p> <p><strong>Overview</strong></p> <p>We are seeking an experienced AI/LLM Engineer to lead the design, customization, and integration of large language models (LLMs) into biomedical research workflows and information retrieval systems. We are looking for someone with hands-on experience training, fine-tuning, augmenting, and deploying LLMs in production environments, ideally within biomedical or life sciences domains. The role is product-oriented and forward-looking, focused on building the next generation of AI-enabled search, retrieval, and knowledge discovery tools.</p> <p>This role serves as a subject matter expert (SME) across multiple product and engineering teams. The selected candidate will help define, architect, and implement LLM-driven capabilities across a portfolio of NCBI services. The position requires strong technical depth, sound architectural judgment, and the ability to collaborate effectively within existing product and technical ecosystems.</p> <p>This is a hands-on, build-oriented role with strategic influence. The candidate must be capable of guiding both what gets built and how it gets built.</p> <p>Only serious candidates accepted - <span style="text-decoration: underline;">we are not seeking candidates that have recently graduated with a masters degree in the past 1-2 years</span>.</p> <p>We are seeking candidates with at least 3+ years experience doing this work after your last degree.</p> <p> </p> <p><strong>Key Responsibilities</strong></p> <ul> <li>Serve as the AI/LLM subject matter expert across product and engineering teams.</li> <li>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).</li> <li>Develop and implement retrieval-augmented generation (RAG) systems integrating LLMs with large-scale biomedical datasets.</li> <li>Provide architectural guidance on model selection, domain adaptation, evaluation strategies, and deployment approaches.</li> <li>Improve model grounding, factual accuracy, and scientific reliability in domain-sensitive applications.</li> <li>Support engineering teams in productionizing AI solutions, ensuring scalability, performance, and maintainability.</li> <li>Evaluate emerging LLM techniques and recommend practical adoption strategies aligned with organizational priorities.</li> </ul> <p><strong>Required Qualifications</strong></p> <ul> <li>3+ years of hands-on experience working with large language models (training, fine-tuning, augmentation, or deployment).</li> <li>Demonstrated experience integrating LLMs into production systems (e.g., semantic search, RAG pipelines, domain-specific QA).</li> <li>Strong experience in ML system architecture and scalable deployment.</li> <li>Proven ability to work cross-functionally with product and technical teams.</li> <li>Experience serving as a technical SME guiding multi-team initiatives.</li> <li>Strong programming skills in Python.</li> <li>Experience with modern ML frameworks (e.g., PyTorch, Hugging Face) and retrieval infrastructure (e.g., embeddings, vector databases).</li> </ul> <p><strong>Preferred Qualifications</strong></p> <ul> <li>Experience building LLM-based systems for biomedical research or life sciences.</li> <li>Familiarity with large scientific corpora, biomedical ontologies, structured knowledge bases, or biological datasets.</li> <li>