Advisor - AI Application Development Engineer – Full-Stack AI Applications

Eli Lilly and Company • Indianapolis, IN

2d • Onsite

About The Position

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’re looking for people who are determined to make life better for people around the world. Organization and Position 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 AI Application Development function is a key horizontal capability within DDCS's Data Sciences & Digital Transformation team, responsible for translating the organization's data and AI strategy into production-grade, user-facing applications that accelerate decision-making across innovation and commercialization verticals. This team partners with data scientists, scientific ML engineers, data engineers, and domain specialists throughout the DDCS matrix to deliver AI solutions that are robust, compliant, and directly tied to program impact. The AI Application Development Engineer will develop, optimize, and fine-tune full-stack AI applications that integrate AI models with front-end user experiences and back-end services to enable workflow automation and decision support across drug delivery science, device design, and development operations. Operating within the DDCS matrix, this role partners with engineers, scientists, and business stakeholders to identify high-value use cases, translate needs into robust AI solutions, and communicate insights to senior technical and business leaders. The engineer collaborates with data scientists and data engineers to ensure modeling approaches and data structures are fit-for-purpose, contributes to the DDCS AI application strategy and architecture, and oversees compliance, security, and risk management across the application lifecycle.

Requirements

• Master's degree in Computer Science, Machine Learning, Data Science, Biomedical Engineering, Mechanical Engineering, Chemical Engineering, Applied Mathematics, or related field
• 5+ years of demonstrated experience developing full-stack software integrating AI models into user-facing products or decision-support tools
• Experience building and deploying AI or machine learning systems, including at least one production deployment.
• Experience with large language model-enabled applications: retrieval-augmented generation (RAG), tool use, and agent-based workflows.
• Strong software engineering skills in Python and one or more additional languages (TypeScript, JavaScript, Java, or C#), including API and microservices design.
• Experience with cloud-based deployment, ML Ops toolchains, observability, and performance/cost optimization in production environments.
• Systems thinking: understanding how data, models, applications, and people interact
• Clear communication of AI capabilities, limitations, and business value to diverse audiences

Nice To Haves

• PhD in relevant field and 2+ years of experience as listed above
• Familiarity with regulated environments and experience implementing security, privacy, documentation, and auditability requirements across the system lifecycle.
• Domain experience in drug delivery, medical devices, combination products, human factors, manufacturing, or quality analytics.
• Demonstrated ability to collaborate effectively across data science, data engineering, and engineering teams.
• Strong written and verbal communication skills with demonstrated ability to present technical concepts to senior technical and business leaders.
• Full-stack AI application design and delivery from prototype to production
• LLM and ML model integration, fine-tuning, and operationalization
• Cross-functional collaboration and use case translation within a matrix organization
• Responsible AI: security, compliance, and model risk management in regulated environments
• Scientific curiosity and domain engagement with drug delivery device development problems

Responsibilities

• AI Application Delivery (Full-Stack) Develop full-stack applications integrating AI models with intuitive front-end interfaces and scalable back-end services for workflow automation and decision support across DDCS programs.
• Design and implement AI application architectures connected to APIs, databases, and enterprise systems, ensuring interoperability and secure data exchange.
• Build and maintain services and APIs that operationalize AI outputs—recommendations, triage, summarization, and anomaly detection—within DDCS workflows.
• Model Enablement & Optimization Fine-tune and embed large language models and machine learning models into production-ready applications using appropriate adaptation techniques.
• Optimize AI solutions for accuracy, latency, robustness, and cost; implement monitoring and continuous improvement mechanisms post-deployment.
• Design and maintain CI/CD workflows for AI application and model lifecycle management, enabling repeatability and scalability.
• Use Case Discovery & Stakeholder Partnership Partner with device engineers, drug delivery scientists, quality, manufacturing, and business stakeholders across the DDCS matrix to understand needs and define high-impact AI use cases.
• Translate stakeholder needs into product requirements, technical designs, acceptance criteria, and delivery plans; clearly communicate trade-offs and recommendations to senior leaders.
• Quantify and communicate uncertainty, sensitivity, and robustness to support risk-based technical decision-making.
• Cross-Functional Data & Modeling Collaboration Collaborate with data scientists and data engineers to ensure data sourcing, structure, and modeling approaches are appropriate for the intended use and decision context.
• Define data requirements, validation strategies, and operational workflows to ensure robust model performance and reproducibility.
• Collaborate with the Scientific ML (SciML) horizontal to develop or apply physics-informed or mechanistic-hybrid machine learning approaches for drug delivery and device performance problems.
• Support data-efficient learning strategies including active learning, Bayesian optimization, and optimal experimental design to reduce testing burden and maximize learning.
• AI Strategy, Architecture, Compliance & Risk Management Contribute to the DDCS AI application strategy and architectural blueprint: reusable patterns, reference architectures, and platform choices.
• Apply responsible AI principles, security controls, and compliance standards throughout the application lifecycle.
• Identify and mitigate risks related to privacy, security, model behavior, and regulatory compliance (FDA, GxP, 21 CFR Part 11).

Benefits

• 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).

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level