Do you want to engineer the platforms that take agentic AI from prototype to production?Lilly USA is scaling AI from prototype to everyday business capability, from how we bring medicines to market to how we help patients access and afford them. The Lilly USA Technology at Lilly Data Analytics & AI team is seeking a Principal AI/ML Engineer to build the agentic AI and machine learning platforms that power this work. You will turn prototypes into reliable, scalable, well-governed production systems. You will also build the data pipelines, partner integrations, and ML engineering foundations that teams reuse across business use cases.
What You'll Be Doing:As a Principal AI/ML Engineer, you will design and build the engineering backbone for the AI and ML systems that serve business teams across Lilly USA: the orchestration frameworks, end-to-end data and model pipelines, serving infrastructure, and the evaluation and monitoring that keep them reliable in production. You will work at the intersection of enterprise data, business operations, field and customer-facing teams, and third-party partner integrations. You will partner closely with data scientists to productionize models and agentic workflows. You will also work with technical product managers, business and field teams, and Technology at Lilly colleagues to bring those solutions into real applications in a secure and auditable way. You will reuse infrastructure and patterns so that each new business use case is faster and cheaper to ship.
Examples of the kinds of problems you might work on:- Agentic AI workflows that help field and commercial teams prepare for engagements, surface insights, and act faster, including multi-agent systems that reason over complex data and recommend the best next step.
- Predictive and machine-learning models that inform decisions about market access, customer outreach, resource allocation, and revenue protection, from anomaly detection to next-best-action.
- LLM-powered solutions that make large volumes of unstructured information usable, using techniques like retrieval-augmented generation (RAG) to ground answers in trusted, cited sources.
- Secure, analytics-ready data pipelines that bring partner and vendor data into our platforms at scale, with the data quality, governance, and auditability a regulated environment demands.
- Shared, reusable AI tooling for deployment, evaluation, and monitoring that multiple teams build on instead of rebuilding, so each new solution ships faster and more reliably than the last.
How You'll Succeed:- Build and operationalize agentic AI platforms, including agent-to-agent orchestration frameworks, multi-agent workflow engines, tool and API integration layers, and secure connectivity between agents, enterprise systems, and external partner platforms.
- Build and integrate data pipelines, bringing data from enterprise systems, external vendors, and third-party partners into secure, analytics-ready platforms.
- Deliver AI capabilities for business use cases, such as reporting, predictive analytics, process and decision optimization, and decision support for field and commercial teams.
- Engineer robust, end-to-end AI/ML pipelines, covering data capture, feature engineering, training, fine-tuning, inference, and feedback, at production scale and reliability.
- Stand up ML engineering foundations, including model and prompt versioning, registries, experiment tracking, continuous integration and delivery for models, and automated evaluation and rollback within each pipeline.
- Apply production-grade deployment techniques, including scalable model serving, containerization, inference optimization, and deployment of small and specialized models hosted in-house, with attention to latency, cost, scalability, and data residency.
- Build observability and monitoring for AI systems, including tracing of agentic workflows, cost and latency tracking, drift detection, and integration of AI evaluation into production monitoring.
- Implement governance and data-protection controls, including privacy and regulatory compliance (e.g., HIPAA where applicable), PHI and PII handling, de-identification, and auditability consistent with a regulated environment.
- Design reusable, horizontal engineering capabilities and shared infrastructure that multiple teams and use cases can build on, rather than one-off deployments.
- Partner with data scientists to productionize prototypes and harden them for scale, reliability, and maintainability, and work with business users to drive adoption.
- Communicate clearly with technical and non-technical stakeholders and stay current with emerging agentic and ML engineering tooling.
- Manage and prioritize multiple projects simultaneously.
What You Should Bring:- Foundational knowledge in communication, leadership, teamwork, problem solving, solution and blueprint definition, business acumen, architectural processes (e.g., blueprinting, reference architecture, governance), technical standards, project delivery, and the industry.
Business Analysis and Technical Leadership, including:- Engaging with the business and proactively seeking opportunities to deliver business value.
- Understanding business requirements and effectively translating business needs and processes into technical terms, and vice versa.
- Eliciting and defining requirements.
- Ensuring appropriate business roles are engaged in solution execution.
- Participating in design reviews to ensure traceability of requirements.
- Networking with appropriate Technology at Lilly colleagues to determine solutions that meet business partners' needs.
- Seeking opportunities to reuse existing processes and services to streamline support and implementation of key systems.
- Staying abreast of tools and technologies to influence Technology at Lilly strategy so that it provides the best usage opportunities for the business.
Basic Qualifications:- Master's degree in Computer Science, Computer Engineering, Data Science, or a related quantitative or technical field, with at least 1 year of professional experience implementing technical systems
- OR Bachelor's degree in Computer Science, Computer Engineering, Data Science, or a related quantitative or technical field, with at least 3 years of professional experience implementing technical systems
- Qualified applicants must be authorized to work in the United States on a full-time basis. Lilly will not provide support for or sponsor work authorization or visas for this role, including but not limited to F-1 CPT, F-1 OPT, F-1 STEM OPT, J-1, H-1B, TN, O-1, E-3, H-1B1, or L-1.
Preferred Qualifications:- Strong proficiency in Python and sound software-engineering practices, including version control, testing, and code review.
- Experience taking ML or data solutions from idea through production deployment, including working with business users to get them adopted.
- End-to-end ML engineering: experience with model and prompt versioning, registries, experiment tracking, continuous integration and delivery for models, automated evaluation in pipelines, and observability for AI systems.
- Production deployment: experience with scalable model serving, containerization, inference optimization, and deployment and serving of large models and small, specialized models, plus fine-tuning and distillation pipelines.
- Agentic platforms: experience engineering agent-to-agent orchestration and multi-agent workflow frameworks with modern tooling (e.g., LangGraph, LangChain, or equivalent), including tool and API integration and secure agent connectivity.
- Data engineering: experience building scalable data pipelines, SQL and NoSQL data stores, distributed processing, vector databases, and retrieval systems.
- Cloud and tooling: experience with AWS or Azure, Databricks, containers, Git, and CI/CD automation.
- Governance: experience with secure, auditable systems, PHI and PII handling, and data-residency constraints.
- Partner integrations: experience integrating data and services from external partners, such as vendors, data providers, or health platforms, through APIs and secure data exchange.
- Domain: familiarity with the U.S. healthcare and pharma landscape (e.g., payers, providers, PBMs, patient access), or a demonstrated ability to ramp quickly in a complex, regulated domain. Experience in healthcare or pharma is a plus.
- Demonstrated ability to deliver reliable systems in complex data domains and to communicate with a geographically dispersed group of business and technical colleagues.
Travel percentage for this position: 0-10%
Actual compensation will depend on a candidate's education, experience, skills, and geographic location. The anticipated wage for this position is
$69,000 - $165,000
Full-time equivalent employees also will be eligible for a company bonus (depending, in part, on company and individual performance). In addition, Lilly offers a comprehensive benefit program to eligible employees, including eligibility to participate in a company-sponsored 401(k); pension; vacation benefits; eligibility for medical, dental, vision and prescription drug benefits; flexible benefits (e.g., healthcare and/or dependent day care flexible spending accounts); life insurance and death benefits; certain time off and leave of absence benefits; and well-being benefits (e.g., employee assistance program, fitness benefits, and employee clubs and activities).Lilly reserves the right to amend, modify, or terminate its compensation and benefit programs in its sole discretion and Lilly's compensation practices and guidelines will apply regarding the details of any promotion or transfer of Lilly employees.
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