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Eli Lilly's Product Research & Development (PR&D) organization is seeking a Technical Lead Architect (R5 level) to design and own the Semantic Layer and Knowledge Graph that unifies fragmented CMC (Chemistry, Manufacturing, and Controls) data across structured systems (LIMS, MES, DMS, ELN) and unstructured scientific content (protocols, batch records, investigation reports). This is a hands-on, implementation-heavy role that combines deep technical ownership with business-facing accountability.
You will build ontologies, knowledge graphs, and agentic AI layers that enable PR&D's agents to reliably retrieve, reason over, and act on unified CMC data. The role ensures the Semantic Layer remains a strategic, PR&D-owned asset that maximizes reuse across data products, reduces fragmentation, and creates measurable business value through faster, more reliable AI-assisted scientific workflows.
Key responsibilities include: mapping structured, semi-structured, and unstructured CMC assets into a connected Knowledge Graph and Vector Database using LLM-assisted tooling; designing and maintaining multiple connected ontologies (structured and business) as PR&D-owned artifacts; owning end-to-end knowledge graph construction including entity/relationship extraction, triplet creation, validation, and graph population across Neo4j/GraphDB and vector stores (Azure AI Search, AWS OpenSearch); building multi-modal and multi-lingual data ingestion pipelines with ontology-based tagging; optimizing hybrid graph, vector, and keyword retrieval for both human exploration and agentic tool calls; applying domain-specific ontologies for semantic tagging and metadata enrichment; designing and deploying agentic architecture (Q&A agent, summarization agent, context retriever) exposed via MCP (Model Context Protocol) servers; designing agent orchestration patterns for knowledge and domain agents to interoperate with evaluation frameworks and observability; partnering with PR&D business and science stakeholders to translate workflow needs into Semantic Layer priorities and owning the technical roadmap; providing hands-on technical leadership to data/knowledge graph engineers while remaining a primary builder; establishing governance, compliance, and architecture review processes ensuring FDA 21 CFR Part 11, GxP, ICH, and enterprise security/privacy requirements; evaluating emerging knowledge graph and agentic AI technologies through staged proof-of-concepts; establishing reusable ontology, triplet, and retrieval design patterns; and mentoring data engineers, platform engineers, and architects on ontology design and knowledge graph engineering.
Required expertise areas include: knowledge graph engineering and optimization, ontology design and knowledge modeling, triplet creation/aggregation/validation, RDF/OWL/SPARQL and property graphs (Neo4j, AWS Neptune), vector databases and embeddings (Azure AI Search, AWS OpenSearch, Pinecone), hybrid and graph-RAG retrieval, unstructured-to-structured data conversion, multi-modal and multi-lingual data ingestion, generative AI and agentic AI, multi-agent architecture, agent-to-agent protocols, agent design patterns (ReAct, Plan-and-Execute, Reflection), MCP, agent evaluation frameworks, agent observability and monitoring, prompt library management, advanced prompt and context engineering, data architecture and data product development, cloud platforms (Azure/AWS), containerization and Kubernetes, security and compliance architecture, API and integration architecture, Python, stakeholder management, and technical team leadership.
REQUIREMENTS:
- Proven track record building production knowledge graphs and semantic layers end-to-end (not overseeing them)
- Demonstrated success implementing complex semantic layers spanning both structured and unstructured data
- Experience driving ontology/knowledge graph technology decisions, retrieval optimization, and architecture governance
- Experience operating multiple agents in production with shared guardrails, evaluation, and observability tooling (not just single-agent RAG/Q&A systems)
- Experience partnering with business/science stakeholders to shape technical roadmaps and prioritization
- Track record providing technical leadership to engineering teams while remaining hands-on
- Track record of successfully leading through ambiguity, complexity, and organizational transformation
ADDITIONAL QUALIFICATIONS:
- Experience in CMC, pharmaceutical, or life sciences data (batch records, process parameters, LIMS/MES/DMS/ELN systems)
- Familiarity with data-to-ontology mapping tooling and document-to-schema conversion frameworks
- Experience exposing knowledge graphs to LLM/agent consumption via MCP or equivalent tool-calling interfaces
- Experience designing agent-to-agent (A2A) communication patterns and multi-agent orchestration beyond single-agent tool calling
- Comfortable presenting technical strategy and trade-offs to senior, non-technical stakeholders
- Background in pharma and GxP-regulated environments
REQUIRED LEADERSHIP CAPABILITIES:
- Able to go deep technically while translating graph/retrieval trade-offs into business-relevant narratives for senior stakeholders
- Comfortable owning and defending a technical roadmap in front of business and executive audiences
- Provides day-to-day technical leadership and mentorship to engineers without requiring a formal people-manager structure
- Systems thinking connecting ontology/graph design choices to downstream agentic workflow and business outcomes
