All Of Our Groups
healthcare
ScientificLead,GenerativeAIEngineer,AppliedIntelligenceforDiscovery
“Scientific Lead, Generative AI Engineer, Applied Intelligence for Discovery at All Of Our Groups. Skills: Generative AI, LLM, RAG, text-to-SQL, agentic workflows, Python, LLM ecosystem, evaluation frameworks. Design, build, and operate the core AI systems. retrieval-augmented generation over internal scientific documents”
Industry & Context.
complex scientific queries
What They're Looking For.
Must Have
PhD in Computer Science, Data Science, or a related technical field with 0-3+ years of or equivalent experience building production LLM, MS in Computer Science, Data Science, or a related technical field with 5+ years of or equivalent experience building production LLM systems
Nice to Have
Experience building LLM-powered applications, including at least two of: RAG systems, text-to-SQL, agentic workflows, or fine-tuning pipelines, software engineering skills in Python with experience building production-grade systems, Deep familiarity with the modern LLM ecosystem: embedding models, vector databases, and orchestration frameworks, Experience designing evaluation frameworks for LLM systems — systematic approaches to measuring accuracy, detecting hallucinations, and tracking regressions, Comfort working with complex, heterogeneous data — databases with hundreds of tables, specialized schemas, or domain-specific vocabularies, Familiarity with cloud computing environments (AWS preferred), containerization (Docker), and CI/CD practices, Experience in pharmaceutical, biotech, or life sciences environments, Familiarity with biomedical data types (omics, clinical, molecular) or scientific databases, Experience with MLOps/LLMOps tooling: experiment tracking, model registries, prompt versioning, A testing for AI systems, Knowledge of biomedical ontologies (Gene Ontology, MeSH, ChEBI) or experience integrating domain-specific knowledge into LLM systems, Experience building for regulated environments where auditability, reproducibility, and explainability are requirements
What You'll Do.
and operate the core AI systems
retrieval-augmented generation over internal scientific documents
text-to-SQL over complex omics databases
agentic workflows that automate multi-step analyses
evaluation infrastructure that enable the next-generation of medicines for patients
and optimize RAG pipelines over internal publications
electronic lab notebooks
and other scientific documents
Build hybrid retrieval systems combining vector search with structured metadata
and ontology-aware filtering
Build and optimize text-to-SQL systems over Lilly’s databases
enabling scientists to query gene expression
and variant data through natural language
Develop schema documentation
and gold-standard question/SQL pairs that bridge how scientists think about data and how it is stored
Implement multi-step reasoning approaches (chain-of-thought
Reflexion loops) to improve accuracy on complex scientific queries
Design agentic AI workflows that chain database queries
and visualization into automated multi-step scientific analyses
Evaluate and integrate emerging orchestration frameworks (LangGraph
custom architectures) for scientific use cases
Build evaluation frameworks measuring accuracy
and scientific validity of AI outputs
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