Hiring: Agentic AI Solutions, Associate Director Company: Eli Lilly and Company Location: Indianapolis, IN Job Posted Time: 2026-09-17 03:57:13 Employment Type: Full-time / Hybrid Target Skills & Keywords : AI, CI/CD, Digital Transformation, Git, LLM, MLOps, Machine Learning, NLP, NumPy, Pandas, Pinecone, PyTorch, Python, R, RAG, SQL, Weaviate, pgvector, scikit-learn About the job Experience: •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. Required Skills: •AI 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. 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. •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. •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. •Operational 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. •Applied hands-on capability in cloud platforms and solid engineering practice: Git, containers, CI/CD, and experiment or run tracking. •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. Compensation: •$118,500 - $173,800 / year •Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance) Interested candidates, please apply directly through the job posting on company's career page or try via AI auto apply on this platform. Don't miss this opportunity to join a forward-thinking team!