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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:
Delivery, Devices, and Connected Solutions (DDCS) sits within Eli Lilly's Product Research \& Development organization. We are a diverse team of scientists and engineers responsible for discovering, designing, and developing patient\-centric drug delivery solutions across a broad range of modalities — from injection devices to novel routes of administration and nanomedicines. DDCS drives the drug delivery innovation agenda across early and late development to meet the needs of an expanding portfolio that spans small molecules, biologics, and nucleic acid therapeutics.
DDCS is organized around a matrix model with strong disciplinary and functional horizontals supporting innovation and commercialization verticals. Our vision is to get our medicines to more patients faster by accelerating reach and scale, guided by three strategic pillars: Delivery Systems, Robust \& Sustainable, and Patient Experience \+ Outcomes.
The Digital Transformation and Data Science team within DDCS serves as a key foundation for DDCS's digital transformation efforts. The team helps make data work more effectively for the organization by turning information into faster insights, stronger decision\-making, improved ways of working, and practical AI solutions embedded in everyday DDCS workflows.
Position Overview:
The Senior Advisor, Agentic AI Solutions Engineer will partner with DDCS business functions to translate machine learning, statistics, scientific computing, and AI concepts into practical tools that improve speed, productivity, and impact across the organization. Operating within the Digital Transformation and Data Science team, this role will design, build, and deploy AI\-enabled workflows, agentic scientific systems, knowledge extraction tools, and scientific ML capabilities that help colleagues turn complex technical information into actionable decisions. Fundamentally, you are energized by extremely large, complicated, real\-world challenges and are excited about full\-stack development developing and utilizing modern AI tools to produce genuine, trusted results.
Key Responsibilities :
AI Solutions Engineering \& Practical Tool Delivery
- Partner with business functions across DDCS to identify, prioritize, and scope high\-value opportunities where AI, machine learning, and automation can improve speed, productivity, insight generation, and decision quality.
- Translate stakeholder needs into practical AI tools, technical designs, acceptance criteria, and delivery plans that fit real scientific, engineering, and operational workflows.
- Develop AI\-enabled applications, services, and workflows that integrate models, data sources, document collections, and user\-facing interfaces for decision support and workflow automation.
Agentic Scientific AI Systems \& Knowledge Extraction
- Create reusable scientific agent skills, task harnesses, validators, run ledgers, and reproducibility controls that allow AI agents to execute diverse, long\-running tasks reliably.
- Build agentic knowledge extraction and question\-answering systems for structured and unstructured technical content, including PDFs, Word documents, handwritten notes, design histories, experimental records, and regulatory\-relevant evidence.
- Design evaluation, monitoring, guardrails, and human\-in\-the\-loop escalation patterns so agentic outputs are auditable, traceable, and appropriate for high\-consequence technical decisions.
- Apply knowledge graphs, data ontologies, and structured knowledge representation where they improve retrieval, traceability, and reuse.
Data Strategy, Decision Support \& Workflow Transformation
- Contribute to DDCS data and AI strategy by identifying reusable patterns, data needs, platform capabilities, and solution architectures that support digital transformation at scale.
- Turn information from experiments, simulations, development documents, and business processes into faster insights, stronger judgment, and improved ways of working across innovation and commercialization efforts.
- Communicate model predictions, evidence, assumptions, limitations, uncertainty, and recommended actions through clear visualizations, decision\-support outputs, and quantitative business cases that influence solution adoption, workflow redesign, platform investments, and portfolio priorities.
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.
- Leverage cloud infrastructure (and HPC/GPU resources where needed) to develop, test, deploy, and scale agentic workflows, document intelligence systems, and analytics applications.
Cross\-Functional Collaboration \& Scientific Translation
- Partner across the DDCS matrix with drug delivery scientists, device engineers, formulation scientists, data scientists, AI application engineers, quality, clinical, regulatory, and business stakeholders.
- Identify and prioritize high\-impact opportunities where AI solutions, scientific ML, agentic workflows, or knowledge extraction can reduce development time, improve productivity, or mitigate technical and business risks.
- Translate complex analytical and AI findings into clear narratives and quantitative business cases that influence solution adoption, workflow redesign, platform investments, and portfolio priorities.
Capability Building, External Leadership \& Mentorship
- Advance the DDCS technology roadmap for practical AI tools, document intelligence, agentic workflows, and reusable knowledge systems.
- Share methods, reference patterns, and lessons learned that help DDCS embed data and AI into everyday work across scientific and business functions.
- 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 DDCS’s practice where they add real value.
Basic Qualifications
- Earned Master’s degree with a minimum 5 years post\-degree experience in Computational/Computer Science, Machine Learning, Artificial Intelligence, Engineering, or a related quantitative field (or equivalent 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 or scientific needs into tractable AI, modeling, or computational workflows.
- Demonstrated ability to frame ambiguous business or scientific needs as tractable AI, modeling, or computational workflows.
- Skill in communicating technical recommendations with clearly stated assumptions, uncertainty, and limitations, to scientific, engineering, and business audiences.
Additional Preferences:
- Earned PhD in relevant field with 2\+ years relevant experience
- Experience applying AI/ML to healthcare, pharmaceutical, or life\-sciences problems (prior biology or life\-sciences background not required).
- Strong SQL and relational data modeling, with comfort turning large, messy, unstructured, or incomplete 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. Comfort with GPU and HPC environments is a plus.
- Experience with knowledge graphs, ontologies, or structured knowledge representation for technical content.
- 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.
- 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 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 to deploy.
- 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 decisions.
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 \- $209,000
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-$209K range is below the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($169K) sits 21% below the category median. Disclosed range: $129K to $209K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Eli Lilly AI Hiring
Eli Lilly has 2 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Boston, MA, US, Indianapolis, IN, US. Compensation range: $209K - $283K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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