AI Solutions Architect

Job Summary #CA-FT

Within Enterprise Analytics & Data Services (EADS), the AI Solutions Architect serves as a senior technical leader and subject matter expert for UC Davis Health’s most complex and strategically important data science and artificial intelligence initiatives within UC Davis Health’s Health Analytics Core (HAC). The position provides end-to-end technical leadership, translating clinical, operational, and research challenges into scalable, production-grade solutions using advanced analytics, machine learning (ML), Natural Language Processing (NLP), generative artificial intelligence (AI), and emerging technologies.

The AI Solutions Architect leads and influences AI/ML architecture, technical strategy, and solution evaluation, development, and operationalization across complex health data ecosystems within clinical, operational, and research environments. The position establishes reusable frameworks, infrastructure, engineering practices, and responsible AI standards while advising institutional leaders on emerging technologies, vendor solutions, and strategic investments.

As a senior individual contributor, the position maintains hands-on technical involvement while leading multidisciplinary initiatives, mentoring technical staff, and advancing institutional capabilities in AI/ML engineering, MLOps, model evaluation, governance, and responsible adoption of data science and AI.

Apply By Date: October 16, 2026 at 11:59pm. Selection activities may start at any time. Minimum Qualifications - For full consideration, applicants are encouraged to upload license and/or certification if required of the position

• Epic Caboodle & Cogito certification required within 6 months of employment
• Master’s degree or higher in a data-analytic or quantitative discipline (e.g., Data Science, Computer Science, Statistics, Mathematics, Biomedical/Health Informatics, Engineering, or an equivalent technical field).
• 8+ years of progressive, applied advanced data analytics solutions, with substantial hands-on expertise in production software engineering, machine learning, scalable data engineering, and enterprise system design.
• Proven track record architecting, engineering, and deploying production-grade Large Language Model (LLM) applications.
• Hands-on experience designing, implementing, and maintaining automated LLM evaluation harnesses, testbeds, and benchmarking frameworks.
• Deep technical expertise spanning both traditional statistical machine learning (e.g., tree-based ensembles, regression) and modern deep learning architectures (e.g., Transformers, sequence models, embeddings). Experience working with multimodal data, such as images and text, to generate actionable insights.
• Advanced proficiency in Python and SQL, with demonstrable mastery of modern data science and AI ecosystems (e.g., PyTorch/TensorFlow, Hugging Face Transformers, LangChain/LangGraph, LlamaIndex, Scikit-learn, MLflow, and vector databases).
• Demonstrated command of modern SDLC best practices, automated CI/CD pipelines, containerization (e.g., Docker), version control systems (e.g., GitLab), and automated testing/monitoring for deployed models and services.
• Practical expertise in transforming, aggregating, and modeling complex, heterogeneous, and high-dimensional structured and unstructured data across modern distributed compute and enterprise data platforms.
• Demonstrated understanding of healthcare operations, clinical workflows, and health data standards, with the ability to translate clinical terminology and care processes into safe, relevant, and actionable AI/ML solutions.

Preferred Qualifications

• 5+ years of specialized experience in healthcare data science or biomedical informatics, with a proven track record of applying advanced statistical modeling, modern machine learning, and production-grade agentic AI architectures to solve complex clinical, operational, and research challenges.
• Hands-on experience architecting and integrating data pipelines with clinical exchange standards and protocols, including HL7 FHIR APIs, USCDI, and medical terminology ontologies coupled with deep proficiency across both relational and non-relational database management systems.
• Extensive practical experience architecting and executing solutions across unified analytics and cloud data platforms, electronic health record (EHR) data environments, and enterprise business intelligence frameworks.
• Pract…