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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.
We are seeking an experienced Senior Principal Data, Analytics & AI Engineer to join the Global DIA team within MQ Tech at Lilly. This role sits at the intersection of data engineering, supply chain domain expertise, and applied AI. You will architect the near-real-time data foundations that AI agents reason over and lead the design of the agents themselves — multi-agent workflows that read across manufacturing and supply chain systems, correlate signals that today sit in disconnected platforms, and put clear, actionable insight in front of the people running the line and the network.
This is a senior principal, hands-on delivery role with technical leadership expectations. You will own solutions end to end, set technical direction for correlating supply chain and manufacturing data through data modernization and agentic AI, and mentor engineers on the team. The solutions you build reach production and are used every shift, bringing transparency and decision support to Lilly's Supply Chain and Manufacturing operations.
Job Responsibilities
Technical leadership and architecture
- Own the technical design and architecture for agentic AI and data modernization solutions across the Global Supply Chain and MQ landscape, from problem framing through production support.
- Set standards and reusable patterns for cloud based data modeling, pipeline design, agent orchestration, and evaluation that other engineers build on.
- Mentor and provide technical guidance to junior and mid-level engineers; lead code and design reviews.
- Partner with senior business stakeholders and IT leadership to shape roadmaps, sequence delivery, and make build/buy trade-offs; direct and hold accountable external vendor and partner teams.
Supply chain data domain and analytics
- Develop and apply deep working knowledge of Global Supply Chain data domains — material master, BOM and recipe, procurement, inventory and stock movements, production orders and batch execution, planning and scheduling, warehouse management, transportation management, distribution and logistics, quality and deviations.
- Work fluently across SAP (S/4HANA, ECC, BW/4HANA — master data, MM, PP, QM, SD, and IBP) and SHARP, translating source-system semantics into trusted, business-ready data models.
- Bring an informed point of view on end-to-end supply chain processes — S&OP, MRP, plan-to-produce, source-to-pay, order-to-cash, capacity and inventory management — and the analytics that support them, including service level, inventory turns, cycle time, schedule adherence, and supply risk.
- Correlate supply chain and manufacturing data — linking ERP, planning, MES, historian, and quality data across systems and time — to produce integrated business solutions that answer questions no single system can, using data modernization and agentic AI.
Agentic AI and machine learning
- Closely collaborate with the business & other global teams on agentic solution design, requirement gathering, solution development and delivery to drive meaningful business value
- Design, build, and deploy AI agents and multi-agent workflows that automate analysis and decision support across manufacturing and supply chain use cases — including prompt engineering, tool and function calling, agent orchestration, and retrieval-augmented generation (RAG) over technical documents and operational data.
- Define how agent performance is measured and improved: test scenarios, evaluation criteria, accuracy and hallucination monitoring, user feedback loops, agent observability, and guardrails that keep agent behavior safe, grounded, and within scope.
- Implement, test, and deploy machine learning and predictive models into production, ensuring performance, scalability, and interpretability.
Data engineering and delivery
- Design, build, and maintain scalable, reliable data pipelines using modern ETL/ELT tooling to ingest, process, and transform large datasets from diverse operational and enterprise sources in support of advanced analytics, reporting, and agentic AI.
- Lead data modernization efforts — migrating and re-platforming legacy supply chain reporting and data assets onto modern lakehouse and cloud architectures.
- Monitor and troubleshoot data quality and agent behavior issues, ensuring data integrity and reliability across all platforms.
- Develop and maintain documentation for data architecture, data flows, agent designs, prompts, model choices, and deployments to support maintainability, validation, and GxP/CSV requirements.
- Ensure compliance with all relevant data privacy, security, and responsible AI requirements.
- Stay abreast of the fast-moving agentic AI and data engineering landscape and bring practical, proven advances into the team's work.
Basic Qualifications
- Bachelor’s in Computer Science, Engineering, Data Science, Statistics, Supply Chain, or related field with 8+ years in data engineering, analytics, or AI/ML, with hands-on production Gen-AI/agentic app delivery.
- Experience in supply chain/manufacturing data, hands-on SAP (S/4HANA/ECC, BW/4HANA) , SHARP, MES and related data and analytics experience.
- Hands-on advanced statistical, machine learning, LLM/agentic app development: prompt engineering, tool/function calling, RAG, vector search/embeddings, multi-step workflows.
- Deep knowledge of agent frameworks (LangChain, LangGraph, or equivalent) and production LLM APIs (OpenAI, Azure OpenAI, Anthropic, Bedrock); Azure/AWS/Databricks.
- Advanced Python (preferred); SQL,Java/Scala/R a plus, PowerBi/Tablue, Web Apps etc
- Experience in end-to-end developing production grade analytics systems.
Preferred Qualifications
- Masters in Computer Science, Engineering, Data Science, Statistics, Supply Chain, or related field with 5+ years in data engineering, analytics, or AI/ML, with hands-on production Gen-AI/agentic app delivery.
- Experience in pharmaceutical, life sciences, or other regulated manufacturing supply chains.
- Track record of taking multiple AI or agentic solutions through to production, including evaluation, monitoring, and iteration after go-live.
- Strong command of manufacturing processes and metrics (OEE, downtime, yield, batch performance, right-first-time) alongside supply chain metrics.
- Experience with supply chain planning software (SAP IBP, Kinaxis, o9) and IoT/streaming operational data.
- Exposure to manufacturing data sources such as historians (OSI PI), MES/TrakSYS, PMX, or LIMS.
- Experience working in a GxP-regulated environment, including CSV and validation practices.
- Experience with data visualization tools (Power BI preferred).
- Applied knowledge of responsible AI practices: model risk, bias, explainability, and data privacy.
- Experience leading globally distributed teams or vendor partners.
- Databricks, AWS or Azure certifications.
Additional Information:
- Position is located in Indianapolis, Indiana
- Full time position 5 days per week onsite
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
$129,000 - $211,200
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 $129K-$211K range is below the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 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 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 11,857 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $228,700. This role's midpoint ($170K) sits 20% below the category median. Disclosed range: $129K to $211K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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: $272,275; 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% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 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 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. 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 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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.
Frequently Asked Questions
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