Sr. Principal Machine Learning Engineer

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.

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, Small Language Models, intelligent agents, and modern   MLOps   practices.   This is a senior individual contributor role requiring deep technical   expertise , strong architectural thinking, and the ability to influence engineering practices across teams. The successful candidate will be expected to build scalable solutions,   establish   engineering best practices, mentor peers, and drive adoption of modern AI and machine learning capabilities.   This role is ideal for an engineer who enjoys solving complex business problems through innovative AI technologies while   maintaining   a strong focus on scalability, reliability, governance, and user value.

AI/ML Solution Architecture and Engineering   Architect and build production-grade machine learning, deep learning, generative AI, and agentic AI systems for commercial and field engagement use cases.   Lead technical   design for   solutions involving retrieval-augmented generation, embeddings, semantic search, LLM orchestration, Text2SQL, recommendation systems, predictive modeling, and intelligent workflow automation.   Design scalable AI services and APIs that integrate with enterprise data platforms, business applications, CRM ecosystems, and downstream product workflows.   Design, train, fine-tune, and deploy neural network models for domain-specific commercial and field use cases, including deep learning architectures such as Transformers, Convolutional Neural Networks (CNNs), Recurrent Neural Networks (RNNs), and LSTMs as applicable.   Develop deep learning solutions that prioritize performance, generalization, explainability, scalability, and production readiness.   Apply frameworks such as   PyTorch   and TensorFlow to build, experiment with, and   productionize   neural network models.   Build modular, reusable, observable, secure, and maintainable solutions aligned with enterprise technology patterns.   End-to-End Model and GenAI Delivery   Own the full lifecycle of AI/ML delivery, including problem framing, data preparation, feature engineering, experimentation, model training, prompt and context design, evaluation, deployment, monitoring, and continuous improvement.   Develop and productionize NLP and LLM capabilities, including RAG, prompt engineering, model adaptation, fine-tuning where appropriate, and response quality evaluation.   Train and fine-tune neural network and language models using full fine-tuning, instruction tuning, domain adaptation, and parameter-efficient techniques such as   LoRA   and   QLoRA   to optimize performance for specific business tasks and datasets.   Apply model distillation techniques to compress large teacher models into smaller, efficient student models, balancing accuracy, latency, infrastructure cost, and operational constraints for production deployment.   Design, train, evaluate, and deploy Small Language Models (SLMs) as lightweight, cost-efficient alternatives to large language models for latency-sensitive, on-device, resource-constrained, or