PR&D – Technical Lead Architect – Semantic Layer & Agentic AI

At Lilly, the work is demanding because patients are waiting. We unite caring with discovery to help make life better for people around the world, knowing that every decision, every detail, and every day matters. Headquartered in Indianapolis, Indiana, our over 50,000 employees around the globe take on complex challenges to discover and deliver life-changing medicines, strengthen how health is understood and managed, and support the communities we serve. This is hard, urgent, selfless work—but it’s work worth doing. If you’re driven by purpose and ready to bring your best to work that truly matters for patients, we invite you to join us.  Product Research & Development (PR&D) · CTI-MD Tech@Lilly Product Research & Development (PR&D) owns the CMC (Chemistry, Manufacturing, and Controls) data that underpins every regulatory submission, batch investigation, and process decision across Lilly's development portfolio, spanning structured systems (LIMS, MES, DMS, ELN) and unstructured scientific content (protocols, batch records, investigation reports). The function operates under strict GxP, data integrity, and audit-readiness requirements. The Technical Lead Architect (R5), PR&D, builds and owns the Semantic Layer and Knowledge Graph that convert fragmented, siloed CMC data into a unified, ontology-driven foundation usable by both humans and AI agents — and the agentic layer that lets PR&D's agents reliably retrieve, reason over, and act on that foundation. This role is hands-on and implementation-heavy — building ontologies, triplets, knowledge graphs, and agent orchestration directly — while also carrying business-facing accountability for the Semantic Layer roadmap and technical leadership of the engineers who build alongside them. This role ensures the Semantic Layer remains a PR&D-owned strategic asset, maximizing reuse across data products, reducing fragmentation, and creating measurable business value through faster, more reliable AI-assisted scientific workflows. Path/Level: R5 (Technical Lead Architect) Key Responsibilities Unified Data Foundation — map structured, semi-structured, and unstructured CMC/PR&D assets into a connected Knowledge Graph and Vector Database, using LLM-assisted tooling to accelerate ontology development and reduce manual curation. Multi-Ontology Design & Ownership — design and maintain multiple connected ontologies — a structured ontology and a business ontology — as PR&D-owned artifacts. Triplet & Knowledge Graph Engineering — own end-to-end knowledge graph construction: entity/relationship extraction, triplet creation, triplet aggregation, triplet validation, and graph population across Neo4j/GraphDB and vector stores (Azure AI Search, AWS OpenSearch, or equivalent). Multi-Modal Data Ingestion — build ingestion pipelines handling multi-modal and multi-lingual PR&D content (documents, structured records, and beyond text-only formats where relevant), with ontology-based tagging and entity/intent recognition feeding both the knowledge graph and vector store. Retrieval Optimization — optimize hybrid graph, vector, and keyword retrieval for both human exploration and agentic tool calls, tuning for precision, recall, latency, and groundedness across KBQ, metadata, and document-level questions answered from a single semantic layer. Ontology-Driven Tagging & Metadata Enrichment — apply domain-specific ontologies for semantic tagging and contextual indexing of PR&D documents; build technical and business metadata enrichment pipelines (table/column descriptions, join conditions, business rules) using LLMs. GenAI & Agentic Architecture — design and deploy an agentic architecture (Q&A agent, summarization agent, context retriever, and similar) exposed via MCP (Model Context Protocol) servers, enabling fast, relevant, and explainable knowledge retrieval for scientific workflows. Agent Orchestration & Operational Guardrails — design orchestration patterns for PR&D's