Associate Vice President, Agentic AI & Autonomous CMC Development

ITSMF; Indianapolis (IN); On-site; USD 250,000 - 400,000; Full time

Job summary

Lilly in Indianapolis, IN, seeks a visionary executive to lead the next paradigm shift in pharmaceutical development: agentic AI. This role aims to create autonomous, AI-enabled development ecosystems where scientists and engineers focus on creativity, judgment and strategy while intelligent agents handle routine knowledge work, analysis, documentation, planning and execution.

The Associate Vice President will define and execute Lilly's vision for Agentic CMC Development, building a pan-DDCS/PRD

Job description

Organization Overview:

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.

Position Overview:

We're looking for a visionary executive to lead the next paradigm shift in pharmaceutical development: agentic AI. This is the shift from human-executed workflows supported by software to autonomous, AI-enabled development ecosystems, where scientists and engineers spend their time on creativity, judgment and strategy while intelligent agents handle routine knowledge work, analysis, documentation, planning and execution.

This Associate Vice President will define and execute Lilly's vision for Agentic CMC Development: building a pan-DDCS/PRD AI capability platform and deploying agentic solutions that transform how development work gets done across drug delivery, devices, combination products, synthetic molecule development, biologics, formulation sciences, process development, analytical sciences, quality, regulatory and the manufacturing interface.

The long-term goal is an Autonomous Development Platform: one that continuously learns from enterprise knowledge, experimental outcomes, development history, manufacturing performance, regulatory interactions and scientific literature, and increasingly executes development work on its own. This platform will enable:

• Agent-driven knowledge discovery and technical intelligence
• Autonomous technical workflow orchestration
• AI-generated development strategies and experiment plans
• Automated documentation and regulatory content generation
• AI-assisted design, optimization, change control planning and risk assessment
• Digital experts and copilots embedded across development functions
• Enterprise-scale scientific knowledge management
• Agentic coordination between development, manufacturing, quality and regulatory functions

Key Responsibilities:

1. Set the Agentic AI Strategy and Roadmap

• Serve as the single point of accountability for DDCS and pan PRD agentic AI strategy, setting a multi-year vision, roadmap and technology strategy for autonomous development systems.
• Define the future-state operating model for an AI-enabled development organization and prioritize the highest-value opportunities for transformation.
• Build the business case and champion narrative that win support from scientists, engineers and senior leaders, addressing both the opportunity and the risks of agentic AI openly and directly.
• Track Lilly's growing portfolio of agentic AI initiatives and external pharma agentic AI moves, translating industry and competitive trends into roadmap decisions.

2. Own the Agentic AI Platform Architecture

• Lead development of an enterprise-scale platform spanning multi-agent orchestration frameworks, large language model ecosystems, agent memory architectures, retrieval-augmented generation and knowledge graph systems, digital twin and simulation integration, and autonomous workflow engines.
• Establish standards for agent-to-agent and tool communication (for example, Model Context Protocol and Agent-to-Agent standards) so agents work together across the pan PRD data and knowledge landscape.
• Build reusable enterprise capabilities that DDCS, BRD, SMDD and Manufacturing can all adopt.
• Build in responsible AI controls from the start: human-in-the-loop quality systems, AI governance and compliance frameworks, and GxP/