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About This Role
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.
Organization Overview
At Lilly, we serve an extraordinary purpose. For nearly 150 years we have worked tirelessly to discover medicines that make life better. In Product Research and Development (PRD), we transform molecules into medicines, working through an increasingly external partner network across drug substance, drug product, and analytical services.
PRD's External Supply Chain & Operations (ESCO) team stewards this external partner network — delivering supplier excellence, systematic and scalable sourcing workflows, supply chain design, technical-transfer processes, and end-to-end network performance across drug substance, drug product, and analytical services.
As ESCO scales a harmonized supplier governance model and moves toward AI-enabled, agentic sourcing operations, it requires a net-new capability: hands-on agentic AI and applied ML expertise dedicated to building practical tools and autonomous agents for PRD's sourcing systems and business processes. This is a new position for the team, modeled on the agentic AI engineering capability DDCS has built within its Digital Transformation and Data Science team, adapted for ESCO's external-sourcing context.
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@Lilly 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@Lilly — 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 Responsibilities
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.
- 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@Lilly 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@Lilly 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@Lilly'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 Outcomes
Success 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.
Lilly is dedicated to helping individuals with disabilities to actively engage in the workforce, ensuring equal opportunities when vying for positions. If you require accommodation to submit a resume for a position at Lilly, please complete the accommodation request form (https://careers.lilly.com/us/en/workplace-accommodation) for further assistance. Please note this is for individuals to request an accommodation as part of the application process and any other correspondence will not receive a response.
Lilly is proud to be an EEO Employer and does not discriminate on the basis of age, race, color, religion, gender identity, sex, gender expression, sexual orientation, genetic information, ancestry, national origin, protected veteran status, disability, or any other legally protected status.
Our employee resource groups (ERGs) offer strong support networks for their members and are open to all employees. Our current groups include: Africa, Middle East, Central Asia (AMECA), Black Employees at Lilly (BE@Lilly), Chinese Culture Network (CCN), EnAble, Evolve, Lilly Indian Network (LIN), Organization of Latinx at Lilly (OLA), Pride (LGBTQ+ Allies), Veterans Leadership Network (VLN) and Women’s Initiative for Leading at Lilly (WILL).
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 compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
#WeAreLilly
Salary Context
This $118K-$173K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 1356 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 8,943 AI roles we're tracking, AI/ML Engineer positions make up 75% of the market. At Eli Lilly, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,866 positions with disclosed compensation. Director-level AI roles across all categories have a median of $272,250. This role's midpoint ($146K) sits 31% below the category median. Disclosed range: $118K to $173K.
Across all AI roles, the market median is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,250; VP: $235,000.
Eli Lilly AI Hiring
Eli Lilly has 5 open AI roles right now. They're hiring across AI/ML Engineer. Based in Indianapolis, IN, US. Compensation range: $143K - $246K.
Location Context
Across all AI roles, 6% (521 positions) offer remote work, while 8,351 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,416 roles, $263,811 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 8,943 open positions tracked in our dataset. By seniority: 175 entry-level, 4,132 mid-level, 3,268 senior, and 1,368 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (521 positions). The remaining 8,351 roles require on-site or hybrid attendance.
The market median for AI roles is $213,380. Top-quartile compensation starts at $264,148. The 90th percentile reaches $319,310. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 8,943 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,739), AI Product Manager (403), AI Software Engineer (402). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (175) are outnumbered by mid-level (4,132) and senior (3,268) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,368 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (521 positions), with 8,351 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,380. Top-quartile roles start at $264,148, and the 90th percentile reaches $319,310. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $253,100 median, while Prompt Engineer roles sit at $141,440. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,422 postings), Aws (808 postings), Azure (638 postings), Rag (555 postings), Gcp (457 postings), Pytorch (433 postings), Claude (341 postings), Kubernetes (337 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
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