Principal AI/ML Engineer – Data Analytics & AI

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, and 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.

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.

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 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 Technology at Lilly colleagues to determine solutions; seeking opportunities to reuse existing processes and services; and staying abreast of tools and technologies to influence Technology at Lilly strategy.