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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.
Senior Data & AI Solutions Architect
Lilly’s Purpose
At Lilly, we unite caring with discovery to make life better for people around the world. We are a global healthcare leader headquartered in Indianapolis, Indiana. Our employees around the world work to discover and bring life-changing medicines to those who need them, improve the understanding and management of disease, and give back to our communities through philanthropy and volunteerism.
We give our best effort to our work, and we put people first. We are looking for people who are determined to make life better for people around the world.
Come help us transform Lilly’s Manufacturing and Quality business!
The Global Data, Analytics and AI team for Manufacturing and Quality is focused on delivering accelerated business value through Durable Analytic Products, Data as a Product, Data Products, and AI Products.
We are looking for a hands-on technical leader who can architect and deliver Data, Analytics, and AI products for Manufacturing & Quality and who brings experience working with Quality Assurance processes in a regulated environment.
As Lilly advances its Quality transformation journey, this role will shape and deliver solutions supporting Quality Assurance, Quality Management Systems, inspection readiness, quality risk management, deviation management, CAPA, complaints, audits, and broader Quality operations.
The successful candidate will combine strong technical depth with product leadership and Quality domain knowledge. This individual will be expected to make architecture decisions, guide engineering teams, contribute directly to solution design and prototyping, and lead products from concept through production deployment and adoption.
What You’ll Be Doing
You will be responsible for defining, designing, building, and scaling data and AI solution architecture for Global Manufacturing and Quality teams. You will lead product-based delivery pods across Data Products, Durable Analytic Products, Advanced Analytics, AI Products, and Agentic AI capabilities.
You will serve as a senior technical leader and solution architect for transformation programs, helping the organization move from fragmented reporting and manual analysis toward scalable, reusable, governed, and intelligent digital capabilities.
This is a hands-on technical leadership role. You will be expected to remain close to the technology, provide technical direction to engineers and data scientists, challenge solution designs, contribute to prototypes and technical problem-solving, and ensure products are engineered for secure, scalable, and sustainable production use.
Success in this role requires strength across architecture, engineering, product delivery, Quality domain understanding, and executive stakeholder engagement.
How You’ll Succeed
Technical Architecture, Data Products, and Engineering
- Define end-to-end architectures for Data Products, Durable Analytic Products, analytics applications, AI Products, and Agentic AI solutions.
- Architect solutions across data ingestion, transformation, orchestration, storage, semantic modeling, analytics, visualization, AI, integration, and application layers.
- Translate Manufacturing and Quality business requirements into practical technical designs, implementation patterns, and product roadmaps.
- Provide hands-on leadership in solution design, data modeling, architecture blueprints, technical spikes, and prototype development.
- Design scalable data pipelines, curated data layers, semantic models, APIs, analytical models, and business-facing applications.
- Define non-functional requirements for security, privacy, performance, scalability, resiliency, observability, cost, and supportability.
- Establish reusable architecture and engineering patterns that can scale across Quality processes, sites, and global business teams.
- Apply modern engineering practices including source control, automated testing, CI/CD, secure development, deployment automation, and production monitoring.
- Evaluate technical options and make clear recommendations based on business value, delivery risk, lifecycle cost, and enterprise alignment.
- Ensure products progress beyond proof-of-concept work into governed, supported, and sustainable production capabilities.
AI, and Agentic Solutions
- Architect and lead the delivery of Machine Learning, Generative AI, and Agentic AI solutions for Manufacturing and Quality.
- Design solutions using technologies such as large language models, embeddings, semantic search, vector stores, retrieval-augmented generation, knowledge graphs, orchestration frameworks, and AI agents.
- Develop capabilities such as signal detection, trend analysis, investigation support, regulatory intelligence, knowledge retrieval, summarization, recommendation generation, and workflow automation.
- Partner with Data Scientists and AI Engineers to define model workflows, evaluation approaches, prompt strategies, grounding methods, and human-in-the-loop controls.
- Determine when a business problem requires traditional analytics, Machine Learning, Generative AI, deterministic automation, or a combination of approaches.
- Design AI capabilities for traceability, explainability, evaluation, monitoring, and responsible use in Quality processes.
- Leverage enterprise AI platforms and reusable services where appropriate while ensuring solutions remain fit for their intended use.
- Evaluate emerging AI technologies through practical experimentation and apply them where they can create measurable business value.
- Help evolve products from descriptive reporting toward proactive intelligence, guided decision-making, and appropriately governed agentic workflows.
Quality and Regulated Solution Delivery
- Apply Quality Assurance experience to the design and delivery of solutions supporting regulated business processes.
- Partner with Quality process owners and subject matter experts to understand intended use, process controls, risk, records, approvals, and decision points.
- Ensure technical solutions account for data integrity, traceability, access control, change management, validation, and operational support requirements.
- Incorporate appropriate human review and approval controls where AI outputs inform Quality or compliance decisions.
- Work with Quality, Cybersecurity, GISQ, Software Assurance, validation, and Responsible AI teams to meet applicable governance requirements.
- Support Quality domains including Quality Management Systems, deviation management, CAPA, complaints, change control, audits, inspection readiness, quality risk management, laboratory operations, and batch release.
- Balance delivery speed with Quality, security, compliance, maintainability, and long-term business value.
Technical Leadership and Business Partnership
- Lead product pods and guide engineers, data scientists, analysts, architects, and external partners through design, development, testing, deployment, and production support.
- Break complex problems into executable technical workstreams, milestones, dependencies, and delivery increments.
- Identify technical risks and integration constraints early and drive resolution across teams.
- Connect technical investments to measurable outcomes such as reduced manual effort, improved cycle time, earlier risk identification, stronger inspection readiness, and better decision-making.
- Challenge fragmented or overly customized approaches and recommend scalable capabilities that can be reused across the Manufacturing and Quality network.
- Serve as a trusted technical advisor to Manufacturing, Quality, Data, AI, and Technology leadership.
- Communicate technical decisions, risks, dependencies, and tradeoffs in clear business language.
- Provide technical oversight for work delivered by vendors, consulting partners, and system integrators.
- Mentor technical team members and help strengthen architecture, engineering, data, and AI capabilities across product teams.
- Maintain a strong quality bar while enabling teams to deliver iteratively and respond quickly to business feedback.
What You Should Bring
Basic Qualifications
- Bachelor’s degree in Computer Science, Engineering, Data Science, Information Systems, or a related technical or scientific discipline.
- 12+ years of relevant experience in solution architecture, data architecture, data engineering, analytics engineering, AI/ML, software engineering, and/or digital product delivery.
- Experience designing and delivering enterprise-scale Data, Analytics, or AI products.
- Experience working with Quality Assurance processes in a pharmaceutical, biotechnology, manufacturing, healthcare, or another regulated environment.
- Experience leading technical teams or product pods through architecture, development, testing, deployment, and production support.
- Strong communication, problem-solving, technical leadership, and stakeholder-management skills.
Preferred Technical Experience
The successful candidate should bring strong experience in several of the following areas:
- Data architecture, data engineering, data integration, data modeling, and modern analytics architecture.
- Cloud platforms such as AWS and/or Azure.
- Technologies such as Databricks, Spark, Redshift, Snowflake, Athena, Postgres, or comparable platforms.
- Scalable data pipelines, curated data layers, semantic models, APIs, analytical applications, and reusable data products.
- Relational, non-relational, and analytical data stores.
- SQL and analysis of complex enterprise data domains.
- Data governance, metadata, lineage, master data, and data-quality practices.
- Git-based source control, CI/CD, automated testing, DevOps, and production observability.
- Secure architecture, role-based access, privacy, monitoring, logging, and operational support.
- Demonstrated ability to translate business requirements into scalable technical solutions.
Preferred AI Experience
- Hands-on or technical leadership experience delivering AI/ML solutions.
- Experience with Generative AI, large language models, prompt engineering, RAG, vector stores, embeddings, semantic search, knowledge graphs, or AI orchestration frameworks.
- Experience with AI/ML frameworks such as TensorFlow, PyTorch, scikit-learn, LangChain, Semantic Kernel, or comparable technologies.
- Experience designing AI solutions with human-in-the-loop controls, explainability, evaluation, monitoring, and governance.
- Understanding of model lifecycle management, AI risk, and Responsible AI practices.
- Ability to distinguish among automation, analytics, decision support, and autonomous workflow opportunities.
Preferred Quality and Regulated Environment Experience
- Experience working within Quality Assurance organizations or other regulated business functions.
- Knowledge of GxP, Quality Management Systems, deviations, CAPA, complaints, change control, audits, inspection readiness, Quality Risk Management, laboratory operations, or batch release.
- Experience delivering technical or AI-enabled solutions within regulated environments.
- Understanding of data integrity, validation, Software Assurance, cybersecurity reviews, electronic records, and AI governance.
Leadership Attributes
- Operates as a technical leader who can move effectively among strategy, architecture, prototyping, and implementation.
- Remains hands-on enough to evaluate designs, challenge technical assumptions, and guide engineering teams.
- Uses business outcomes and intended use to drive architecture decisions.
- Makes clear, evidence-based technical recommendations.
- Demonstrates learning agility, intellectual curiosity, and interest in emerging technologies.
- Builds effective relationships across business, Quality, engineering, architecture, and governance teams.
- Develops others through technical mentoring, coaching, and constructive design feedback.
Additional Preferences
- Quality Assurance Experience
- Knowledge of GxP, Pharmaceutical manufacturing processes and automations systems.
About the Organization:
Lilly IT builds and maintains capabilities using cutting edge technologies like most prominent tech companies. What differentiates Lilly IT is that we create new possibilities through tech to advance our purpose – creating medicines that make life better for people around the world, like data driven drug discovery and connected clinical trials. We hire the best technology professionals from a variety of backgrounds, so they can bring an assortment of knowledge, skills, and diverse thinking to deliver innovative solutions in every area of our business. Lilly is entering an exciting period of growth, and we are committed to delivering innovative medicines to patients around the world. Lilly is investing around the world to create new state-of-the-art manufacturing site’s and continue expanding our existing facilities to created capacity required to continue with our mission. The brand-new facilities will utilize the latest technology, advanced highly integrated and automated manufacturing systems, and have a focus on minimizing the impact to our environment.
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
$153,000 - $246,400
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 $153K-$246K range is above 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 ($199K) sits 6% below the category median. Disclosed range: $153K to $246K.
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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