Agentic AI Solutions, Associate Director

Lilly • Indianapolis, IN

2d • $118,500 - $173,800 • Onsite

About The Position

Lilly’s Product Research and Development External Supply Chain & Operations (ESCO) team manages an external partner network across drug substance, drug product, and analytical services. As the team expands its supplier governance model and adopts AI-enabled sourcing operations, this newly created role will build practical agentic AI, applied machine learning, and document-intelligence capabilities for ESCO’s sourcing systems and business processes.

This is a hands-on, full-stack builder role. The Associate Director will personally design, develop, and deploy AI-enabled applications, workflows, and agents that help colleagues turn complex CDMO/CMO information into actionable sourcing decisions. The work includes automating analysis of proposals, capacity information, technical-transfer records, audit and quality documents, contracts, and supplier scorecards. The role partners with Tech@Lilly and PRD Strategy where enterprise platforms and standards are involved, and bridges ESCO, procurement, PRD functional groups, and supply-chain design.

A core deliverable is building the PRD Supply Chain Intelligence (AI) agent from feasibility through pilot to production. The role also establishes reliable agent skills, evaluation and monitoring practices, responsible-AI safeguards, and reusable knowledge systems. It communicates evidence, assumptions, limitations, uncertainty, and recommendations to support adoption and high-consequence sourcing and supply-continuity decisions. The position is on-site at Lilly Technology Center-North in Indianapolis, Indiana, with 0–10% travel.

Requirements

• Earned master’s degree and at least 5 years of post-degree experience in computational or computer science, machine learning, artificial intelligence, engineering, or a closely related quantitative STEM field.
• At least 2 years of applied technical experience building AI or machine-learning solutions in a programming language such as Python or R, with working knowledge of ecosystems such as NumPy, pandas, PyTorch, or scikit-learn.
• At least 3 years of expertise in strategic thinking, problem framing, and translating ambiguous business needs into tractable AI, modeling, or automation workflows.
• Demonstrated ability to frame ambiguous sourcing or supply-chain business needs as tractable AI, modeling, or computational workflows.
• Ability to communicate technical recommendations to sourcing, procurement, and business audiences, stating assumptions, uncertainty, and limitations clearly.

Nice To Haves

• Earned PhD in a relevant field and at least 2 years of post-degree experience in computational or computer science, machine learning, artificial intelligence, engineering, or a quantitative field.
• Experience applying AI or machine learning to pharmaceutical, life-sciences, supply-chain, or sourcing problems; prior biology or life-sciences background is not required.
• Familiarity with pharmaceutical or regulated-industry supply chains.
• Strong SQL and relational data-modeling skills, with comfort turning large, messy, unstructured, or incomplete sourcing data into reliable, decision-ready output.
• Hands-on experience with cloud platforms and sound engineering practices, including Git, containers, CI/CD, and experiment or run tracking.
• Experience with knowledge graphs, ontologies, or structured knowledge representation for supplier, contract, or technical content.
• Fluency with agent frameworks and orchestration such as LangGraph, AutoGen, CrewAI, or equivalent, and underlying concepts including planner/executor splits, hand-offs, escalation logic, and state management across multi-step or multi-session workflows.
• End-to-end RAG design over commercial and technical documents, including PDF, scan, and table parsing and layout extraction; chunking; hybrid search; reranking; embedding models; and vector stores such as pgvector, Pinecone, or Weaviate.
• LLM engineering judgment in context design, tool/function calling (MCP or comparable standards), structured-output design at scale, and choosing between fine-tuning, retrieval, and prompting.
• Evaluation-engineering experience with golden datasets and benchmarks, automated regression suites, failure-mode tracking, and evidence of a trustworthy agent shipped to production.
• Production LLMOps experience, including treating cost, latency, and reliability as engineering constraints and monitoring them accordingly.
• Experience designing guardrails and safety for autonomous systems, including approval gates, rollback logic, hallucination and drift detection, and model-risk approaches for high-consequence sourcing decisions.
• Evidence of contributions to significant work, ideally publications at relevant ML, AI, or NLP venues such as NeurIPS, ICML, ICLR, ACL, or EMNLP, or comparable open-source or applied contributions.
• Self-directed approach and comfort operating in a new, evolving capability area with limited precedent.
• Strong business acumen and ability to work effectively in ambiguity.

Responsibilities

• Partner with ESCO sourcing operations leads and category managers to identify, prioritize, and scope opportunities for agentic AI, machine learning, and automation.
• Translate stakeholder needs into practical AI tools, technical designs, acceptance criteria, and delivery plans suited to sourcing, supplier-management, and technical-transfer workflows.
• Build and deploy AI-enabled applications, services, and workflows integrating models, sourcing data, document collections, and user-facing interfaces.
• Create reusable agent skills, task harnesses, validators, run ledgers, and reproducibility controls for reliable execution of long-running sourcing tasks.
• Build agentic knowledge-extraction and question-answering systems for structured and unstructured sourcing content, including proposals, quality and audit reports, contracts, capacity data, technical-transfer records, and supplier scorecards.
• Design evaluation, monitoring, guardrails, and human-in-the-loop escalation patterns to make agent outputs auditable, traceable, and appropriate for consequential decisions.
• Apply knowledge graphs, data ontologies, and structured knowledge representation where they improve retrieval, traceability, and reuse.
• Build the PRD Supply Chain Intelligence (AI) agent from feasibility through pilot to production, coordinating with Tech@Lilly and PRD Strategy.
• Turn information from CDMO/CMO documents, sourcing systems, and business processes into faster insights and improved ESCO ways of working.
• Communicate model outputs, evidence, assumptions, limitations, uncertainty, and recommended actions through clear visualizations and decision-support materials.
• Champion software-engineering practices, including version control, automated testing, CI/CD, containers, documentation, reproducibility, observability, and fit-for-purpose MLOps and agent-ops.
• Develop validation, monitoring, documentation, and model-risk approaches aligned with intended use, responsible-AI principles, GxP awareness, and applicable regulatory expectations.
• Use cloud infrastructure to develop, test, deploy, and scale agentic workflows, document-intelligence systems, and analytics applications.
• Partner with ESCO, PRD Strategy, Procurement, PRD functional leaders, and Tech@Lilly to align tools with enterprise data and platform standards.
• Coordinate with Tech@Lilly’s formal CRO data exchange and digital workflow for CMC lab experiments, adopting existing exchange pathways rather than creating parallel ones.
• Translate complex AI findings into clear narratives and business cases that influence adoption, workflow redesign, and sourcing-team priorities.
• Advance ESCO’s technology roadmap for agentic AI tools, document intelligence, and reusable knowledge systems, using DDCS’s Digital Transformation and Data Science team as a collaboration partner and reference.
• Share methods, reference patterns, and lessons learned to help embed agentic AI into everyday sourcing work.
• Mentor team members and partners on reliable agentic systems, responsible AI, and communication of model assumptions, uncertainty, and decision impact.
• Stay current with agentic AI and LLM developments and bring useful tools, frameworks, and techniques into ESCO practice.

Benefits

• Company bonus eligibility, depending in part on company and individual performance.
• Eligibility to participate in a company-sponsored 401(k).
• Pension.
• Vacation benefits.
• Eligibility for medical, dental, vision, and prescription drug benefits.
• Flexible benefits, including healthcare and/or dependent day-care flexible spending accounts.
• Life insurance and death benefits.
• Certain time-off and leave-of-absence benefits.
• Well-being benefits, including employee assistance program, fitness benefits, and employee clubs and activities.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior