Senior AI and Large Language Model (LLM) Engineer
Leads design, customization, and integration of LLMs into biomedical research workflows and NCBI platforms like PubMed. Requires 3+ years hands-on LLM experience with Python, PyTorch, Hugging Face, and RAG systems; biomedical domain preferred.
About the job
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).
Preferred Qualifications
- 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.
Skills
Python, PyTorch, Hugging Face, RAG, LLMs, Vector Databases, Embeddings, Machine Learning, Bioinformatics, Retrieval Augmented Generation
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