• Design, build, and deploy production-grade machine learning models and systems at scale • Own the full ML lifecycle: from problem definition and feature engineering to training, evaluation, deployment, and monitoring • Build scalable data and model pipelines with strong reliability, observability, and automated retraining • Work with large-scale datasets to improve ranking, recommendations, search relevance, prediction, content/user understanding, and optimization systems. • Partner cross-functionally with Product, Data Science, Infrastructure, and Engineering teams to translate complex problems into ML solutions • Improve system performance across latency, throughput, and model quality metrics • Research and apply state-of-the-art machine learning and AI techniques, including deep learning, graph & transformers based, and LLM evaluation/alignment • Contribute to technical strategy, architecture, and long-term ML roadmap

Role Description The Senior AI/ML Engineer is a hands-on builder who owns the highest-complexity agentic and LLM-enabled work in the Finance Shared Services pillar. This role designs and implements multi-agent systems, intelligent document processing at scale, and advanced predictive or generative solutions that extend the program beyond traditional RPA. The Senior AI/ML Engineer mentors AI/ML Engineers, partners tightly with the Solution Architect, and represents the pillar in cross-program AI practice forums. - Lead design and implementation of high-complexity agentic and LLM-enabled solutions, including UiPath Maestro-based orchestration and multi-agent systems. - Build and deploy advanced ML, LLM, and agentic components end-to-end, with production-grade reliability and observability. - Define evaluation, monitoring, and responsible AI practices for pillar solutions, aligned to Mayo Clinic standards. - Partner with the Solution Architect on architecture, reuse, and standards, and with delivery teams on implementation. - Mentor AI/ML Engineers across the pillar and support their growth. - Represent Finance Shared Services in cross-pillar AI practice forums; share patterns with Revenue Cycle and IA Enablement. - Evaluate emerging AI capabilities and drive adoption decisions for the pillar. Benefits - Medical: Multiple plan options. - Dental: Delta Dental or reimbursement account for flexible coverage. - Vision: Affordable plan with national network. - Pre-Tax Savings: HSA and FSAs for eligible expenses. - Retirement: Competitive retirement package to secure your future. Company Description Mayo Clinic is top-ranked in more specialties than any other care provider according to U.S. News & World Report. As we work together to put the needs of the patient first, we are also dedicated to our employees, investing in competitive compensation and comprehensive benefit plans to take care of you and your family, now and in the future. And with continuing education and advancement opportunities at every turn, you can build a long, successful career with Mayo Clinic. - Today, our employees are located at our three major campuses in Phoenix/Scottsdale, Arizona, Jacksonville, Florida, Rochester, Minnesota, and at Mayo Clinic Health System campuses throughout Midwestern communities, and at our international locations. - Each Mayo Clinic location is a special place where our employees thrive in both their work and personal lives.

Role Description The AI/ML Engineer, a full-stack AI/ML architect for the Revenue Cycle pillar, is responsible for both agentic solutions (UiPath Maestro, LLM orchestration, agent + bot + human-in-the-loop patterns) and predictive ML solutions. This role sets the AI architecture direction for Revenue Cycle, defines reference patterns, and collaborates with delivery teams to bring solutions from prototype to production. The ideal candidate is equally comfortable designing an agentic orchestration topology and shipping a production ML model. Key Responsibilities - Architect full-stack AI/ML solutions for Revenue Cycle, spanning agentic orchestration (UiPath Maestro, LLM agents, RPA bots, human-in-the-loop) and predictive ML. - Define reference architectures, design patterns, and guardrails for AI use in Revenue Cycle, in partnership with enterprise architecture, security, and data governance. - Lead design of high-complexity solutions such as agentic patient portal inbasket management, combining UiPath Maestro, AI agents, RPA bots, and human-in-the-loop oversight. - Develop, evaluate, and productionize ML models using Python, modern ML libraries, and Mayo Clinic's approved cloud and data platforms. - Partner with delivery leads, app analysts, and business analysts to translate Revenue Cycle opportunities into AI-enabled solutions with measurable ROI. - Establish standards for model lifecycle management, monitoring, bias and drift evaluation, and responsible AI practice aligned to Mayo Clinic policy. - Evaluate emerging AI capabilities (new LLM models, agentic frameworks, UiPath releases) and guide when and how to adopt them. - Mentor AI/ML engineers and developers across the program; contribute to enterprise AI community of practice. Benefits - Medical: Multiple plan options. - Dental: Delta Dental or reimbursement account for flexible coverage. - Vision: Affordable plan with national network. - Pre-Tax Savings: HSA and FSAs for eligible expenses. - Retirement: Competitive retirement package to secure your future. Equal Opportunity All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, gender identity, sexual orientation, national origin, protected veteran status or disability status. Learn more about the "EOE is the Law". Mayo Clinic participates in E-Verify and may provide the Social Security Administration and, if necessary, the Department of Homeland Security with information from each new employee's Form I-9 to confirm work authorization.

• Designing model evaluation pipelines for models in development and production • Designing user studies for subjective model evaluations. • Converting requirements into measurable metrics. • Designing and developing automated evaluation dashboard to see model performances and compare results. • Training new models to capture new and different evaluation metrics. • Communicating with the model team to help design better models based on the evaluation results. • Communicating with the data team to help decide the type of data necessary to improve model performance. • Communication with the product-manager to make sure product requirements are correctly measured. • Help grow the evaluation team as the founding member. • Lead the evaluation team in the future.