Systems Engineer - Field Solutions, Lilly USA Tech@Lilly

About the Role - Systems Engineer

We are looking for a curious, hands-on Systems Engineer to join the Lilly USA field solutions team. This is an AI-first role: you will build AI-powered products for the field and use AI throughout your own engineering work. In this role you will design, build, integrate, and run the sales applications that our field teams depend on, with our Field-Facing AI Assistant as the primary focus. You will own solutions end to end: understanding what the business needs, shaping the technical design, building and integrating it, releasing it in a regulated environment, and keeping it healthy in production. You will work closely with product owners, architects, platform and data teams, vendors, and business partners across Lilly USA. This is an on-site role based at Lilly Technology Center - South in Indianapolis, Indiana.

AI-First Engineering: How This Team Works

• Build AI products: design and ship LLM-powered features and agents for the Field-Facing AI Assistant using foundation models such as Claude on Amazon Bedrock or Azure OpenAI, orchestration frameworks such as LangGraph or the Databricks Mosaic AI Agent Framework, and retrieval patterns such as RAG over Databricks Vector Search, or similar technologies.
• Engineer with AI every day: use coding assistants such as Claude Code, GitHub Copilot, or Cursor for development, test generation, debugging, and documentation, and treat prompt engineering and context engineering as core skills.
• Connect agents to enterprise systems: build tool and data integrations using standards such as Model Context Protocol (MCP) and agent-to-agent (A2A) patterns, or comparable approaches.
• Operate AI responsibly: implement evaluation harnesses, guardrails, observability and tracing (for example MLflow or LangSmith), and prompt and model version management so AI features meet Lilly AI governance and regulated-environment standards.
• Find the next opportunity: identify where AI agents and automation remove manual work for field users and the engineering team, prototype quickly, and prove value with data.

Key Responsibilities

• Design, build, and maintain our Field-Facing AI Assistant and other sales-facing applications and the integrations that connect them to data and enterprise platforms.
• Translate business and functional requirements from field, sales operations, and product partners into scalable, secure, and supportable technical designs.
• Develop and maintain APIs, data flows, and integration patterns that make field applications interoperable with the broader Lilly USA technology ecosystem.
• Own release readiness for your solutions in a regulated environment: automated testing, CI/CD pipelines, environment management, change control, and validation documentation where required.
• Keep production healthy through monitoring, alerting, root cause analysis, and support readiness, and drive improvements that reduce incidents and manual effort.
• Build and ship AI capabilities for the field: LLM-powered features, retrieval pipelines, and reusable agents using foundation models and agent frameworks (for example Claude on Amazon Bedrock, Azure OpenAI, LangGraph, or Databricks Mosaic AI, or similar technologies).
• Participate in technical and architecture reviews, raise risks and dependencies early, and help set engineering standards for the field solutions team.
• Partner with vendors and offshore delivery teams to deliver and support solutions, reviewing their work for quality and alignment with Lilly standards.
• Maintain clear technical documentation, including solution designs, integration specifications, and runbooks.
• Lead solution design across multiple systems, provide day-to-day technical direction and mentorship to other engineers and delivery partners, and influence architecture and platform strategy for Lilly USA field solutions.

Basic Qualifications

• Bachelor's degree in computer science, information technology, or a related technical field.
• 2+ years of experience in information technology or software engineering.
• At least 1 year of hands-on experience building and operating data and application workloads on Databricks or similar analytics platform (for example notebooks, jobs, Delta Lake, SQL, and Python/PySpark).
• Qualified candidates must be legally authorized to be employed in the United States. Lilly does not anticipate providing sponsorship for employment visa status (e.g., H-1B or TN status) for this employment position.

Preferred Qualifications

• Hands-on experience building front-end applications in React with TypeScript or JavaScript.
• Experience building or integrating generative AI or LLM-based solutions (for example with Claude, OpenAI, or other foundation models) and familiarity with agentic AI concepts.
• Experience building or integrating APIs and working with relational and/or NoSQL databases.
• Experience delivering technology solutions and integrations through the full lifecycle, from design through production support.
• Working knowledge of cloud platforms such as AWS or Azure.
• Experience with CI/CD pipelines, automated testing, and version control (Git).
• Strong communication, collaboration, and problem-solving skills, and the ability to explain technical trade-offs to non-technical partners.
• Experience with sales or field-facing applications in pharmaceutical or life sciences.
• Experience in a regulated industry, including work on GxP-validated or SOX-relevant systems and formal change control.
• Production experience with GenAI or LLM-powered features, including retrieval-augmented generation, evaluation and guardrails, and agentic patterns (for example MCP, A2A, LangGraph, or similar frameworks and standards).
• Experience with infrastructure-as-code (Terraform, CloudFormation, or similar) and container-based development (Docker, Kubernetes).
• Familiarity with identity and access management, SSO and federation, and API management or gateway tooling.
• Deeper Databricks experience such as Unity Catalog, Model Serving, Databricks Apps, or Mosaic AI, and experience with event-driven or batch data pipelines.
• Experience working with global teams and offshore delivery models across time zones.
• Agile or product-team delivery experience; relevant cloud, AI, or architecture certification (for example AWS Solutions Architect, AWS AI Practitioner, or Azure equivalent).
• Demonstrated ownership of solution architecture across multiple systems and experience providing technical direction and mentorship to other engineers.