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.

Sr. Principal AI/ML Engineer - Market Access Intelligence, LVA, Lilly USA Tech

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.

Why This Role

Lilly Value & Access (LVA) builds the AI and analytics that shape how our medicines reach the patients who need them - informing payer coverage, real-world evidence, pricing and reimbursement, and market access strategy. This is a hands-on senior engineering role: you will personally design, build, and ship production GenAI and ML systems that decision-makers use every day. You'll work on the hardest, highest-value problems in the LVA portfolio - agentic workflows, retrieval over real-world data, Text2SQL, and predictive analytics on complex payer and outcomes data - and see your work move directly into the hands of business, medical, and access teams.

If you want to build (not just advise), work at the frontier of applied GenAI, and do it where the impact is measured in patient access, this is your role.

Role Overview

You will work closely with product managers, software engineers, data scientists, architects, and business stakeholders to develop production-ready AI systems leveraging machine learning, deep learning, large language models, retrieval-augmented generation (RAG), Small Language Models (SLMs), intelligent agents, and modern ML Ops practices.

This is a senior individual-contributor role that stays hands-on keyboard. You will spend the majority of your time building - architecting, coding, training, evaluating, and deploying - while providing light technical mentoring to a small number of engineers within your squad. Success means shipping scalable, reliable, well-governed AI solutions that deliver real user value across the LVA portfolio.

What You'll Build

• Agentic and LLM systems for market access, real-world evidence, and value/access workflows - multi-agent orchestration, tool use, and intelligent workflow automation.
• RAG and Text2SQL over real-world data (RWD), market access datasets, and enterprise knowledge sources - embeddings, semantic search, hybrid retrieval, and reranking.
• Predictive and ML models supporting payer, formulary, book-of-business, and health-outcomes analytics.
• Production AI services and APIs that integrate with enterprise data platforms, business applications, and downstream product workflows.

Key Responsibilities

AI/ML Solution Architecture and Engineering

• Architect and build production-grade ML, deep learning, generative AI, and agentic AI systems for LVA market access and engagement use cases.
• Lead technical design for solutions involving RAG, embeddings, semantic search, LLM orchestration, Text2SQL, recommendation, predictive modeling, and workflow automation.
• Design scalable AI services and APIs that integrate with enterprise data platforms and product workflows.
• Design, train, fine-tune, and deploy neural network models (Transformers, CNNs, RNNs, LSTMs as applicable) using PyTorch (TensorFlow a plus), with a focus on performance, generalization, explainability, scalability, and production readiness.
• Build modular, reusable, observable, secure, and maintainable solutions aligned with enterprise technology patterns.

End-to-End Model and GenAI Delivery

• Own the full AI/ML lifecycle: problem framing, data prep, feature engineering, experimentation, training, prompt and context design, evaluation, deployment, monitoring, and continuous improvement.
• Develop and productionize NLP and LLM capabilities - RAG, prompt engineering, model adaptation, and fine-tuning where appropriate (full, instruction, domain adaptation, and parameter-efficient methods such as LoRA/QLoRA).
• Apply model distillation, compression, and quantization to balance accuracy, latency, cost, and operational constraints; evaluate and deploy SLMs for latency-sensitive, cost-sensitive, or specialized use cases.
• Evaluate trade-offs across foundation, fine-tuned, distilled, and small language models to pick the right architecture for business, operational, and governance needs.
• Implement evaluation frameworks for predictive, generative, and retrieval systems (accuracy, relevance, groundedness, hallucination risk, latency, cost, robustness).
• Monitor production performance, data drift, model drift, failures, and usage; drive remediation and optimization.

Data, Platform, and MLOps Engineering

• Build and operate scalable data and feature pipelines on cloud-native and enterprise data platforms.
• Implement MLOps/LLMOps practices: CI/CD, model registry, experiment tracking, version control, reproducibility, automated testing, observability, lineage, and auditability.
• Work primarily with AWS and Databricks (SageMaker, MLflow, Kubernetes, Docker, and related enterprise-approved tooling).
• Partner with data engineering and platform teams to ensure data quality, governance, lineage, access control, and operational reliability.

Business Partnership and Product Impact

• Partner with product owners, business stakeholders, data scientists, architects, compliance, and engineering to translate market access opportunities into AI/ML product capabilities.
• Shape technical approaches for high-value use cases across analytics, decision intelligence, evidence generation, and workflow automation.
• Communicate model behavior, trade-offs, risks, and recommendations clearly to technical and non-technical audiences.
• Bring practical AI/ML feasibility, scalability, governance, and value considerations into roadmap planning.

Responsible AI, Security, and Governance

• Apply secure-by-design, privacy-by-design, and responsible AI principles across the lifecycle.
• Ensure appropriate controls for explainability, traceability, bias awareness, grounding, auditability, and human oversight.
• Collaborate with governance, compliance, quality, and risk partners to meet standards for data use, reliability, documentation, and operational readiness; maintain architecture, design, evaluation, and support documentation for production AI systems.

Technical Mentoring (light)

• Provide hands-on technical guidance to a small number of AI/ML engineers within your squad.
• Review designs, code, and architecture choices to improve quality and consistency; share reusable patterns and best practices through practical examples.

Required Qualifications

• 6+ years building and deploying production machine learning or AI solutions.
• 3+ years with generative AI - LLMs, RAG, embeddings, prompt engineering, model evaluation, and agentic workflows.
• Strong proficiency in Python and SQL.
• Hands-on experience building and fine-tuning deep learning models with PyTorch (TensorFlow preferred).
• Experience with modern AI/ML frameworks such as PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, DSPy, or equivalent.
• Experience designing and deploying AI services on AWS, Azure and GCP.
• Experience implementing MLOps/LLMOps: CI/CD, monitoring, observability, experiment tracking, model management, and automated testing.
• Strong software-engineering fundamentals: API development, system design, version control, code reviews, and testing.
• Strong understanding of neural network architectures, transfer learning, model optimization, evaluation, and production deployment.
• Experience with structured, semi-structured, and unstructured data at scale.
• Ability to communicate complex technical concepts to technical and non-technical audiences.
• Demonstrated ability to translate ambiguous business challenges into scalable technical solutions that deliver measurable value.

Preferred Qualifications

• Experience in commercial pharma, healthcare analytics, market access, real-world data (RWD), payer/formulary data, or value & access decision-support platforms.
• Experience with PySpark, Databricks, SageMaker, Azure ML, or similar enterprise AI/data platforms.
• GenAI patterns: multi-agent systems, tool use, Text2SQL, semantic search, knowledge graphs, hybrid retrieval, reranking, context engineering, and LLM evaluation.
• Fine-tuning and efficiency: instruction tuning, LoRA/QLoRA, model compression, distillation, quantization, and Small Language Model development.
• Optimizing AI systems across quality, latency, throughput, scalability, and cost.
• Serving frameworks (FastAPI, Flask); Kubernetes, Docker, GitHub Actions, MLflow, vector databases, Spark/PySpark, lakehouse architectures.
• Responsible AI practices: explainability, bias detection, grounded generation, guardrails, auditability, human-in-the-loop.
• Executive-facing dashboards / AI observability using Power BI, Plotly, Dash, or similar.
• M.Tech, MS, or higher in Computer Science, AI, ML, Data Science, or a related quantitative discipline preferred.

Key Skills

Machine Learning Engineering · Generative AI & LLM Engineering · Agentic & Multi-Agent Workflows · Retrieval-Augmented Generation · Prompt & Context Engineering · Text2SQL & Semantic Search · Small Language Models (SLMs) · Deep Learning & Neural Network Design (Transformers, CNNs, RNNs, LSTMs) · Model Fine-Tuning (LoRA/QLoRA, Instruction Tuning, Domain Adaptation) · Model Distillation, Compression & Quantization · Model Evaluation & Error Analysis · Model Monitoring & Drift Detection · Python, SQL, PySpark · PyTorch & TensorFlow · AWS, Databricks, SageMaker · MLOps / LLMOps · CI/CD, Docker, Kubernetes, Git · API Development & AI Service Integration · Responsible AI, Security, Privacy & Governance

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