Data & AI Architect - Tech@Lilly Manufacturing & Quality

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At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism. We give our best effort to our work, and we put people first. We're looking for people who are determined to make life better for people around the world. Role Purpose The Data & AI Architect is a senior Tech@Lilly role built on a foundational conviction: AI systems are only as capable as the data foundations beneath them. At the Alzey greenfield manufacturing site, this role is responsible for designing and governing the data architecture that makes 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 defines the data models, integration patterns, governance structures, and cloud platform standards that give Alzey a reliable, AI-ready data foundation from day one. 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. Alzey is positioned as a digital-native site within the Lilly PDN network. The Data & AI Architect role is a cornerstone of that positioning - setting patterns that will influence the broader network while being directly accountable for Alzey's operational readiness. Key Objectives / Deliverables 1. AI-Ready Data Architecture Design and own the Alzey site data architecture: canonical data models for manufacturing (batch, equipment, process parameters), quality (deviations, CAPAs, specifications), and supply chain domains. Define the data integration strategy connecting site systems (MES, LIMS, DCS, SAP) into a coherent, queryable data layer that directly enables downstream AI/ML and analytics use cases. Establish naming conventions, data ownership, metadata standards, and master data governance frameworks ensuring data is clean, consistent, and AI-consumable at source. Design data lifecycle policies covering retention, archival, lineage tracking, and GMP data integrity compliance (ALCOA+ principles) across all site data domains. Ensure Alzey's data platform architecture aligns with Lilly enterprise cloud standards (Azure/AWS) while remaining fit for the operational realities of a manufacturing site. 2. AI Systems Architecture & Platform Design Define the AI platform architecture for the site: how enterprise AI capabilities (Copilot, QRIUS.AI, Claude-based agentic tools) 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, deviation classification, 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, where human review is mandatory, and how decisions are logged for auditability. Evaluate and select AI/ML tools, vendor solutions, and platform integrations relevant to pharmaceutical manufacturing; provide architectural recommendations to site and network leadership. 3. Data Governance & AI Governance Establish the Alzey data governance framework: data stewardship model, data quality KPIs, issue resolution processes, and periodic review cadence. Define the site AI governance framework: use-case risk classification, model performance monitoring, drift detection, and periodic review of deployed AI systems. Ensure all data and AI implementations comply with Lilly information security, privacy (GDPR), GxP data integrity requirements, and applicable EU AI Act obligations. Maintain appropriate documentation for AI systems used in or adjacent to regulated processes; support computerized system validation (CSV/GAMP5) activities for AI-enabled tools. Serve as the site's primary interface for data and AI-related audits, regulatory inspections, and technical review boards. 4. Site & Network Influence Act as the Alzey representative in Lilly enterprise data and AI architecture forums; contribute Alzey patterns as reusable reference architectures for the broader PDN network. Partner with Concord, RTP, and SES site counterparts to identify shared data challenges, harmonize ontologies, and promote consistent AI deployment patterns across the network. Continuously scan the pharmaceutical AI landscape; interface with external thou

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