The Opportunity
Today, ESCO sourcing leads spend significant time manually working through CDMO/CMO proposals, capacity data, technical-transfer records, audit and quality documentation, contracts, and supplier scorecards scattered across email, SharePoint, and Teams - to answer questions such as which partners have capacity for a given modality next quarter, how a partner performs across modalities, or where a program's technical transfer stands and what is blocking it. This role builds the agentic AI tools and systems that turn that manual, document-heavy work into fast, reliable, AI-assisted decisions.
The Agentic AI Solutions Lead is a hands-on, full-stack build role responsible for designing, building, and deploying AI-enabled workflows, agentic sourcing systems, and document-intelligence tools that help ESCO colleagues turn complex CDMO/CMO information into actionable sourcing decisions. This is a builder role, not an architecture-and-handoff role: the individual personally develops and ships working agents and applications, in partnership with Tech[redacted] and PRD Strategy where enterprise platforms and standards intersect.
The role bridges ESCO, PRD Strategy's End-to-End Supply Chain Design Process, Procurement, and Tech[redacted] - fluent in both the business realities of external sourcing (RFx, technical transfer, supplier governance, capacity planning) and the applied AI/ML engineering needed to automate them responsibly and at scale.
Key ResponsibilitiesAI Solutions Engineering & Practical Tool Delivery- Partner with ESCO sourcing operations leads and category managers to identify, prioritize, and scope high-value opportunities where agentic AI, machine learning, and automation can improve speed, productivity, insight generation, and decision quality across the CDMO/CMO network.
- Translate stakeholder needs into practical AI tools, technical designs, acceptance criteria, and delivery plans that fit real sourcing, supplier-management, and technical-transfer workflows.
- Build and deploy AI-enabled applications, services, and workflows that integrate models, sourcing data sources, document collections, and user-facing interfaces for decision support and workflow automation.
Agentic Sourcing AI Systems & Knowledge Extraction- Create reusable agent skills, task harnesses, validators, run ledgers, and reproducibility controls that allow AI agents to execute diverse, long-running sourcing tasks reliably (e.g., capacity assessment, RFx analysis, technical-transfer status tracking).
- Build agentic knowledge extraction and question-answering systems for structured and unstructured sourcing content, including CDMO proposals, quality/audit reports, contracts, capacity data, technical-transfer records, and supplier scorecards.
- Design evaluation, monitoring, guardrails, and human-in-the-loop escalation patterns so agentic outputs are auditable, traceable, and appropriate for high-consequence sourcing and supply-continuity decisions.
- Apply knowledge graphs, data ontologies, and structured knowledge representation where they improve retrieval, traceability, and reuse across the supplier and CDMO network.
Decision Support & Workflow Transformation- Build the PRD Supply Chain Intelligence (AI) agent end to end - from feasibility through pilot to production - as a core deliverable of this role, in coordination with Tech[redacted] and PRD Strategy's End-to-End Supply Chain Design Process.
- Turn information from CDMO/CMO documents, sourcing systems, and business processes into faster insights, stronger judgment, and improved ways of working across ESCO.
- Communicate model outputs, evidence, assumptions, limitations, uncertainty, and recommended actions through clear visualizations and decision-support outputs that influence solution adoption and sourcing decisions.
Reliability, Validation, MLOps & Responsible AI- Champion software engineering best practices including version control, automated testing, CI/CD, containers, documentation, reproducibility, observability, and fit-for-purpose MLOps/agent-ops practices.
- Develop validation, monitoring, documentation, and model-risk approaches aligned with intended use, responsible AI principles, GxP awareness, and regulatory expectations where applicable to sourcing and supply-chain decisions.
- Leverage cloud infrastructure to develop, test, deploy, and scale agentic workflows, document-intelligence systems, and analytics applications supporting ESCO.
Cross-Functional Collaboration & Scientific/Business Translation- Partner across ESCO, PRD Strategy, Procurement, PRD functional leaders (Drug Substance, Drug Product, Analytical, Packaging), and Tech[redacted] to align agentic tools with enterprise data and platform standards, including the Portfolio Intelligence Hub and the CS&D Supply Chain Control Tower.
- Coordinate with Tech[redacted]'s formal CRO data exchange and digital workflow for CMC lab experiments so ESCO's agentic tools adopt existing digital exchange pathways rather than creating parallel ones.
- Translate complex AI findings into clear narratives and business cases that influence solution adoption, workflow redesign, and sourcing-team priorities.
Capability Building, External Leadership & Mentorship- Advance ESCO's technology roadmap for practical agentic AI tools, document intelligence, and reusable knowledge systems, drawing on DDCS's Digital Transformation and Data Science team as a cross-org reference and collaboration partner.
- Share methods, reference patterns, and lessons learned that help ESCO embed agentic AI into everyday sourcing work.
- Mentor team members and partners on reliable agentic systems, responsible AI, and rigorous communication of model assumptions, uncertainty, and decision impact.
- Stay current with the fast-moving agentic AI and LLM landscape and bring new tools, frameworks, and techniques into ESCO's practice where they add real value.
First-Year OutcomesSuccess in this role is defined by what exists after hire:- By month 6: First agentic AI pilot (e.g., CDMO capacity or technical-transfer status assistant) live with named users; document-intelligence extraction pipeline operating over a defined CDMO/CMO document set; PRD Supply Chain Intelligence agent feasibility assessment complete with a go/no-go recommendation.
- By month 12: PRD Supply Chain Intelligence agent in production pilot with defined evaluation criteria and monitoring in place; at least one additional agentic workflow adopted into standard ESCO sourcing operations; reusable agent skills and evaluation framework established for future ESCO AI use cases.
Basic Qualifications- Earned Master's degree and a minimum of 5 years of post-degree experience in Computational/Computer Science, Machine Learning, Artificial Intelligence, Engineering, or closely related STEM quantitative field.
- 2+ years of applied technical work building AI or machine learning solutions in a programming language such as Python/R, with working knowledge of the ecosystem (NumPy, pandas, PyTorch, scikit-learn, or related).
- 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.
- Skill in communicating technical recommendations with clearly stated assumptions, uncertainty, and limitations, to sourcing, procurement, and business audiences.
Additional Preferences- Earned PhD in a relevant field and 2+ years of post-degree experience in Computational/Computer Science, Machine Learning, Artificial Intelligence, Engineering, or quantitative field.
- Experience applying AI/ML to pharmaceutical, life-sciences, or supply-chain/sourcing problems (prior biology or life-sciences background not required).
- Familiarity with pharmaceutical or regulated-industry supply chains preferred; partners closely with sourcing subject-matter experts across ESCO to build necessary modality- and process-specific context.
- Strong SQL and relational data modeling, with comfort turning large, messy, unstructured, or incomplete sourcing data into reliable, decision-ready output.
- Hands-on experience with cloud platforms and solid engineering practice: 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 (LangGraph, AutoGen, CrewAI, or equivalent) and the primitives underneath them: planner/executor splits, hand-offs, escalation logic, and state management across multi-step or multi-session workflows. Knowing why they fail, not just how to call them.
- End-to-end RAG design over messy commercial and technical documents: parsing and layout extraction from PDFs, scans, and tables; chunking strategy; hybrid search; reranking; embedding models; and vector stores (pgvector, Pinecone, Weaviate, or similar).
- LLM engineering judgment: context design, tool/function calling (MCP or comparable standards), structured output design at scale, and knowing when to fine-tune versus retrieve versus prompt.
- Evals engineering: golden datasets and benchmarks, automated regression suites, and failure-mode tracking. Evidence of a trustworthy agent shipped to production.
- LLMOps in production, treating cost, latency, and reliability as engineering constraints with the monitoring to match.
- Guardrail and safety design for autonomous systems: approval gates, rollback logic, hallucination and drift detection, and model-risk thinking for high-consequence sourcing decisions.
- Evidence of contribution to significant work, ideally through publications at relevant ML/AI/NLP venues (NeurIPS, ICML, ICLR, ACL, EMNLP) or comparable open-source or applied contributions.
- Self-directed and comfortable operating in a net-new, still-evolving capability area with limited existing precedent.
- Strong business acumen and ability to operate effectively in ambiguity.
Additional Information- Travel: 0 to 10%.
- Position Local: Indianapolis, IN; Lilly Technology Center-North (LTC-N). This is an on-site position.
Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$118,500 - $173,800
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly's
