AI-Ready Data Architecture
· 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.
AI Systems Architecture & Platform Design
· 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, 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.
· Deploy enterprise standard platforms and tools to deliver RTP use cases.
Data Governance & AI Governance
· Establish the RTP data governance framework, including the data stewardship model, data quality KPIs, issue resolution processes, and periodic review cadence.
· Establish the RTP AI solution framework, defining development lifecycle milestones, roles and responsibilities.
· Define the site AI governance framework, covering 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 and GxP data integrity requirements.
· 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.
· Act as the RTP representative in Lilly enterprise data and AI architecture forums; contribute RTP patterns as reusable reference architectures for the broader PDN network.
· Partner across peer manufacturing sites and with global MQ partners to identify shared data challenges, harmonize ontologies, and promote consistent AI deployment patterns across the network.
· Partner with site cross-functional teams (e.g. engineering) to ensure AI and analytics roadmaps align with established architectures and solution footprint standards.
· Continuously scan the pharmaceutical AI landscape and interface with external thought leaders and technology vendors to bring relevant advances to the site and network.
· Communicate architecture decisions, data strategy progress, and AI adoption status clearly to site leadership, Tech@Lilly management, and cross-functional business partners.
· Act as the site AI ambassador and lead AI adoption across site functions.
· Develop an in-depth understanding of site business processes and work with business stakeholders to identify opportunities to maximize value through digitization, analytics, and AI.
Champion best practices within the RTP site
· Build data and AI literacy among site teams by translating architectural choices into practical guidance for engineers, quality professionals, and operators who interact with AI-enabled tools.
· Mentor junior data and digital team members on data modelling, integration patterns, and responsible AI deployment principles.
· Define and track KPIs for data quality, platform reliability, and use-case value realization. Report outcomes to site and Tech@Lilly leadership.