Data & AI Architect

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.

The Data & AI Architect is a senior Tech at Lilly role built on a foundational conviction: advanced analytics and AI systems are only as capable as the data foundations beneath them. At the RTP digital manufacturing site, this role is responsible for designing and governing the data architecture that makes analytics and AI adoption possible, scalable, and trustworthy — from manufacturing execution and quality systems to predictive analytics and adaptative workflows.

The role spans two interconnected domains. As Data Architect, the incumbent follows, and provides input to, MQ standards for data models, integration patterns, governance structures, and cloud platform standards that enable a reliable and scalable AI-ready data foundation. As AI Architect, the incumbent translates that foundation into deployable AI capabilities: evaluating tools and platforms, designing human-in-the-loop workflows for GMP contexts, and ensuring AI outputs are reproducible, auditable, and fit for a regulated manufacturing environment.

RTP is positioned as a digital-native site within Lilly, meaning it brings a data rich environment and business areas primed for leveraging that data. The Data & AI Architect role is a cornerstone of that positioning — setting patterns that will influence the broader network while being directly accountable for RTP’s operational excellence.

• Maintain the RTP site data architecture, including canonical data models for manufacturing (batch, equipment, process parameters), quality (deviations, CAPAs, specifications), quality control (methods, samples, results), and supply chain domains.
• Adopt global MQ data integration strategies and architectures and influence the evolution of those strategies to enable RTP-specific downstream AI/ML and analytics use cases.
• Establish and maintain naming conventions, metadata standards, and master data governance frameworks to ensure data is clean, consistent, and AI-consumable at source.
• Own data lifecycle policies covering retention, archival, lineage tracking, and GMP data integrity compliance (ALCOA+ principles) across all site data domains. Align these policies with Manufacturing and Quality standards.
• Ensure RTP’s data platform architecture aligns with Lilly enterprise cloud standards (Azure/AWS) while remaining fit for the operational realities of a manufacturing site.

• Define the AI platform architecture for the site, including how enterprise AI capabilities (Copilot, Claude-based agentic tools, Coretex) are configured, integrated, and governed at the site layer.
• Architect AI-enabled workflows for high-value manufacturing and quality use cases: LLM-assisted batch record review, materials management, predictive maintenance, visual inspection, and electronic logbook analysis.
• Design prompt engineering standards, retrieval-augmented generation (RAG) patterns, and grounding strategies that connect LLMs to site-specific structured and unstructured data.
• Define agentic workflow boundaries for GMP contexts: where AI acts autonomously,…