Principal AI/ML Engineer – Data Analytics & AI

Eli Lilly and Company; Indianapolis (IN); On-site; USD 69,000 - 165,000; Full time

Job summary

Eli Lilly and Company is seeking a Principal AI/ML Engineer to design and build the backbone for AI/ML systems across Lilly USA, including orchestration, end-to-end data and model pipelines, and secure production deployment.

You will work with data scientists, product managers, and business users to productionize prototypes, ensure governance, and deliver scalable, auditable AI capabilities across the organization.

Qualifications

• Master's or Bachelor's degree with required professional experience in technical systems.
• Strong proficiency in Python and sound software-engineering practices (version control, testing, code review).
• End-to-end ML engineering: model/versioning, registries, experiment tracking, CI/CD, observability.

Responsibilities

• Build agentic AI platforms and multi-agent workflow engines with secure connectivity.
• Develop secure, analytics-ready data pipelines from enterprise systems and external partners.
• Deliver AI capabilities for business use cases and productionize prototypes with stakeholders.

Skills

Python

ML engineering

Production deployment

Agentic platforms

Data pipelines

Cloud/AWS

Governance

Education

Master's degree in CS/CE/Data Science

Bachelor's degree in CS/CE/Data Science

Tools

AWS

Azure

Databricks

Kubernetes

CI/CD

Job description

What You'll Be Doing:

As a Principal AI/ML Engineer, you will design and build the engineering backbone for the AI and ML systems that serve business teams across Lilly USA: the orchestration frameworks, end-to-end data and model pipelines, serving infrastructure, and the evaluation and monitoring that keep them reliable in production. You will work at the intersection of enterprise data, business operations, field and customer-facing teams, and third-party partner integrations. You will partner closely with data scientists to productionize models and agentic workflows. You will also work with technical product managers, business and field teams, and Technology at Lilly colleagues to bring those solutions into real applications in a secure and auditable way. You will reuse infrastructure and patterns so that each new business use case is faster and cheaper to ship.

Examples of the kinds of problems you might work on:

• Agentic AI workflows that help field and commercial teams prepare for engagements, surface insights, and act faster, including multi-agent systems that reason over complex data and recommend the best next step.
• Predictive and machine-learning models that inform decisions about market access, customer outreach, resource allocation, and revenue protection, from anomaly detection to next-best-action.
• LLM-powered solutions that make large volumes of unstructured information usable, using techniques like retrieval-augmented generation (RAG) to ground answers in trusted, cited sources.
• Secure, analytics-ready data pipelines that bring partner and vendor data into our platforms at scale, with the data quality, governance, and auditability a regulated environment demands.
• Shared, reusable AI tooling for deployment, evaluation, and monitoring that multiple teams build on instead of rebuilding, so each new solution ships faster and more reliably than the last.

How You'll Succeed:

• Build and operationalize agentic AI platforms, including agent-to-agent orchestration frameworks, multi-agent workflow engines, tool and API integration layers, and secure connectivity between agents, enterprise systems, and external partner platforms.
• Build and integrate data pipelines, bringing data from enterprise systems, external vendors, and third-party partners into secure, analytics-ready platforms.
• Deliver AI capabilities for business use cases, such as reporting, predictive analytics, process and decision optimization, and decision support for field and commercial teams.
• Engineer robust, end-to-end AI/ML pipelines, covering data capture, feature engineering, training, fine-tuning, inference, and feedback, at production scale and reliability.
• Stand up ML engineering foundations, including model and prompt versioning, registries, experiment tracking, continuous integration and delivery for models, and automated evaluation and rollback within each pipeline.
• Apply production-grade deployment techniques, including scalable model serving, containerization, inference optimization, and deployment of small and specialized models hosted in-house, with attention to latency, cost, scalability, and data residency.
• Build observability and monitoring for AI systems, including tracing of agentic workflows, cost and latency tracking, drift detection, and integration of AI evaluation into production monitoring.
• Implement governance and data-protection controls, including privacy and regulatory compliance (e.g., HIPAA where applicable), PHI and PII handling, de-identification, and auditability consistent with a regulated environment.
• Design reusable, horizontal engineering capabilities and shared infrastructure that multiple teams and use cases can build on, rather than one-off deployments.
• Partner with data scientists to productionize prototypes and harden them for scale, reliability, and maintainability, and work with business users to drive adoption.
• Communicate clearly with technical and non-technical stakeholders and stay current with emerging agentic and ML engineering tooling.
• Manage and prioritize multiple projects simultaneously.

What You Should Bring:

• Foundational knowledge in communication, leadership, teamwork, problem solving, solution and blueprint definition, business acumen, architectural processes (e.g., blueprinting, reference architecture, governance), technical standards, project delivery, and the industry.
• Business Analysis and Technical Leadership, including:
  • Engaging with the business and proactively seeking opportunities to deliver business value.
  • Understanding business requirements and effectively translating business needs and processes into technical terms, and vice versa.
  • Eliciting and defining requirements.
  • Ensuring appropriate business roles are engaged in solution execution.
  • Participating in design reviews to ensure traceability of requirements.
  • Networking with appropriate Technology at Lilly colleagues to determine solutions that meet business partners' needs.
  • Seeking opportunities to reuse existing processes and services to streamline support and implementation of key systems.
  • Staying abreast of tools and technologies to influence Technology at Lilly strategy so that it provides the best usage opportunities for the business.

Basic Qualifications:

• Master's degree in Computer Science, Computer Engineering, Data Science, or a related quantitative or technical field, with at least 1 year of professional experience implementing technical systems
• OR Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a related quantitative or technical field, with at least 3 years of professional experience implementing technical systems

Preferred Qualifications:

• Strong proficiency in Python and sound software-engineering practices, including version control, testing, and code review.
• Experience taking ML or data solutions from idea through production deployment, including working with business users to get them adopted.
• End-to-end ML engineering: experience with model and prompt versioning, registries, experiment tracking, continuous integration and delivery for models, automated evaluation in pipelines, and observability for AI systems.
• Production deployment: experience with scalable model serving, containerization, inference optimization, and deployment and serving of large models and small, specialized models, plus fine-tuning and distillation pipelines.
• Agentic platforms: experience engineering agent-to-agent orchestration and multi-agent workflow frameworks with modern tooling (e.g., LangGraph, LangChain, or equivalent), including tool and API integration and secure agent connectivity.
• Data engineering: experience building scalable data pipelines, SQL and NoSQL data stores, distributed processing, vector databases, and retrieval systems.
• Cloud and tooling: experience with AWS or Azure, Databricks, containers, Git, and CI/CD automation.
• Governance: experience with secure, auditable systems, PHI and PII handling, and data-residency constraints.
• Partner integrations: experience integrating data and services from external partners, such as vendors, data providers, or health platforms, through APIs and secure data exchange.
• Domain: familiarity with the U.S. healthcare and pharma landscape (e.g., payers, providers, PBMs, patient access), or a demonstrated ability to ramp quickly in a complex, regulated domain. Experience in healthcare or pharma is a plus.
• Demonstrated ability to deliver reliable systems in complex data domains and to communicate with a geographically dispersed group of business and technical colleagues.

About the Organization:

Technology at Lilly builds and maintains capabilities with cutting-edge technologies, as the most prominent tech companies do. Technology at Lilly is different because we use technology to advance our purpose: creating medicines that make life better for people around the world. Examples include data-driven drug discovery, connected clinical trials, and AI that helps patients access and afford their medicines. 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.

Travel percentage for this position: 0–10%

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 $69,000 - $165,000.

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.

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