Sr. Principal Machine Learning Engineer

End-to-End Model and GenAI Delivery

Data, Platform, and MLOps Engineering

Business Partnership and Product Impact

Responsible AI, Security, and Governance

Technical Leadership and Coaching

Required Qualifications

• 6+ years of experience building and deploying production machine learning or AI solutions.
• 3+ years working with generative AI technologies, including LLMs, RAG architectures, embeddings, prompt engineering, model evaluation, and agentic workflows.
• Strong proficiency in Python and SQL.
• Hands-on experience building and fine-tuning deep learning models using PyTorch ; TensorFlow experience preferred.
• Experience with modern AI/ML frameworks such as PyTorch , TensorFlow, scikit-learn, Hugging Face, LangChain , DSPy , or equivalent technologies.
• Experience designing and deploying AI services on AWS, Azure, or GCP.
• Experience implementing MLOps or LLMOps practices including CI/CD, monitoring, observability, experiment tracking, model management, and automated testing.
• Strong software engineering fundamentals including API development, system design, version control, code reviews, and testing.
• Strong understanding of neural network architectures, transfer learning, model optimization, model evaluation, and production deployment considerations.
• Experience working 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 business value.
• Demonstrated experience leading technical initiatives and influencing architectural decisions across teams.

Preferred Qualifications

• Experience with commercial pharma, healthcare analytics, CRM, customer engagement, decision-support platforms, HCP data, field engagement workflows, targeting/orchestration, or Veeva-related ecosystems.
• Experience with enterprise AI/data platforms such as Databricks, SageMaker, Azure ML, or similar.
• Experience with GenAI patterns such as multi-agent systems, tool use, Text2SQL, semantic search, knowledge graphs, hybrid retrieval, reranking, context engineering, and LLM evaluation.
• Experience with foundation model fine-tuning, instruction tuning, LoRA , QLoRA , model compression, model distillation, quantization, and Small Language Model development.
• Experience evaluating and optimizing AI systems across quality, latency, throughput, scalability, and cost dimensions.
• Experience with FastAPI , Flask, or similar frameworks for model/API serving.
• Experience with Kubernetes, Docker, GitHub Actions, MLflow , vector databases, Spark, PySpark , or lakehouse architectures.
• Familiarity with responsible AI practices such as explainability, bias detection, grounded generation, guardrails, auditability, and human-in-the-loop design.
• Experience creating executive-facing dashboards, model insights, or AI observability views using tools such as Power BI, Plotly , Dash, or similar.
• Strong documentation, design-review, and technical governance habits.
• M.Tech , MS, or higher degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related quantitative discipline preferred.

Key Skills

• Machine Learning Engineering • Small Language Models (SLMs) • Deep Learning and Neural Network Architecture Design • Model Evaluation and Error Analysis • Generative AI and LLM Engineering • Python, SQL, PySpark • Retrieval-Augmented Generation • Databricks, SageMaker, AWS/Azure • Agentic AI and Multi-Agent Workflows • MLOps / LLMOps • Prompt Engineering and Context Engineering • CI/CD, Docker, Kubernetes, Git • PyTorch and TensorFlow • Model Monitoring and Drift Detection • Transformer Models, CNNs, RNNs, and LSTMs • API Development and AI Service Integration • Model Fine-Tuning, Instruction Tuning, and Domain Adaptation • Responsible AI, Security, Privacy, and Governance • LoRA , QLoRA , and Parameter-Efficient Fine-Tuning • Business-facing Technical Leadership • Model Distillation, Compression, and Quantization
• 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 does not discriminate on the basis of age, race, color, religion, gender, sexual orientation, gender identity, gender expression, national origin, protected veteran status, disability or any other legally protected status.