Senior Advisor, Agentic AI Solutions Engineer - Eli Lilly
Software Engineer
We're looking for a Software Engineer focused on designing and building scalable technical solutions. This senior role requires 2+ years of relevant experience.
About the role
Partner with business functions across DDCS to identify, prioritize, and scope high-value opportunities where AI, machine learning, and automation can improve speed, productivity, insight generation, and decision quality.
Translate stakeholder needs into practical AI tools, technical designs, acceptance criteria, and delivery plans that fit real scientific, engineering, and operational workflows.
Develop AI-enabled applications, services, and workflows that integrate models, data sources, document collections, and user-facing interfaces for decision support and workflow automation.
Create reusable scientific agent skills, task harnesses, validators, run ledgers, and reproducibility controls that allow AI agents to execute diverse, long-running tasks reliably.
Build agentic knowledge extraction and question-answering systems for structured and unstructured technical content, including PDFs, Word documents, handwritten notes, design histories, experimental records, and regulatory-relevant evidence.
Design evaluation, monitoring, guardrails, and human-in-the-loop escalation patterns so agentic outputs are auditable, traceable, and appropriate for high-consequence technical decisions.
Apply knowledge graphs, data ontologies, and structured knowledge representation where they improve retrieval, traceability, and reuse.
Contribute to DDCS data and AI strategy by identifying reusable patterns, data needs, platform capabilities, and solution architectures that support digital transformation at scale.
Turn information from experiments, simulations, development documents, and business processes into faster insights, stronger judgment, and improved ways of working across innovation and commercialization efforts.
Communicate model predictions, evidence, assumptions, limitations, uncertainty, and recommended actions through clear visualizations, decision-support outputs, and quantitative business cases that influence solution adoption, workflow redesign, platform investments, and portfolio priorities.
Champion software engineering best practices including version control, automated testing, CI/CD, containers, documentation, reproducibility, observability, and fit-for-purpose MLOps/agent-ops practices.
Develop validation, monitoring, documentation, and model-risk approaches aligned with intended use, responsible AI principles, GxP awareness, and regulatory expectations where applicable.
Leverage cloud infrastructure (and HPC/GPU resources where needed) to develop, test, deploy, and scale agentic workflows, document intelligence systems, and analytics applications.
Partner across the DDCS matrix with drug delivery scientists, device engineers, formulation scientists, data scientists, AI application engineers, quality, clinical, regulatory, and business stakeholders.