Data & AI Architect – Tech@Lilly Manufacturing & Quality

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), Gx P 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 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 local Tech@Lilly AI ambassador, and Lead AI adoption across functions.
• 5. Enablement & Capability Building
• Build data and AI literacy among site teams: translate 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 AI use‑case value realization; report outcomes to site and Tech@Lilly leadership.
• Basic Qualifications

Education & Experience

• Bachelor’s degree in Computer Science, Information Technology, Engineering, or a related technical field.
• 8+ years of experience in data architecture, data engineering, or technology roles, with at least 3 years in a manufacturing or pharmaceutical operations context.
• Demonstrated track record of designing large-scale data models (conceptual, logical, physical, dimensional) for operational and analytical environments.
• Demonstrated experience delivering AI or advanced analytics solutions, in a regulated industry preferably; practical exposure to LLM-based tools, ML pipelines, or intelligent automation.
• Data Architecture & Engineering
• Strong proficiency in data modelling methodologies: 3NF relational design, dimensional modelling, and ontology/semantic graph construction.
• Experience with data integration patterns (ETL/ELT, API-based, event-driven) and industrial data connectivity (OPC-UA, Historian, MES integration).
• Working knowledge of cloud data platforms (Azure Data Lake, Azure Synapse, AWS Redshift/Athena, or equivalent) and associated security and access control models.
• Familiarity with data quality frameworks, master data management (MDM), and data lineage tooling.
• Demonstrated SQL proficiency and experience with data modelling tools (ER/Studio, Erwin, or equivalent).
• AI & Machine Learning Architecture
• Practical working knowledge of large language models (LLMs) and AI assistant platforms; ability to evaluate architectural fit across regulated business use cases.
• Proficiency in prompt engineering: system instruction design, chain-of-thought patterns, and retrieval-augmented generation (RAG) architecture for grounding LLMs in site data.
• Understanding of agentic AI architectures and tool‑use patterns; ability to design appropriate human‑in‑the‑loop controls for GMP‑adjacent workflows.
• Familiarity with the AI/ML lifecycle: data preparation, feature engineering, model selection, validation, deployment, monitoring, and retraining.
• Ability to assess AI output reliability, hallucination risk, and reproducibility requirements in the context of pharmaceutical manufacturing and quality processes.
• Pharmaceutical & Regulatory Knowledge
• Knowledge of GMP data integrity requirements (ALCOA+) and computerized system validation (CSV/GAMP5) as they apply to AI-enabled and data-driven tools.
• Familiarity with applicable regulatory frameworks for AI in regulated industries: EU AI Act, FDA AI/ML guidance, ICH Q9/Q10 quality risk management.
• Understanding of pharmaceutical manufacturing operations and quality management systems (batch records, deviation management, CAPA, change control).
• Leadership & Communication
• Demonstrated ability to communicate complex data and AI architecture concepts to non‑technical stakeholders including site operations, quality, and senior leadership.
• Proven ability to influence cross‑functional teams and network counterparts without direct authority.
• Strong analytical, problem‑solving, and structured communication skills.
• High learning agility; comfortable operating at the frontier
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