Generative AI Solutions Engineer

Job Description Summary The Generative AI Solutions Engineer develops and supports data integrations, reporting, automation, and AI-enabled solutions with an emphasis on enterprise data and analytics. The position is responsible for supporting the initial design, development and operationalization of enterprise AI agents that serve student success needs. Working across the organization, the Generative AI Solutions Engineer, supports data governance, quality monitoring, metadata, lineage, tagging, and traceability to trusted sources. Key Responsibilities: AI Solutions and AI-Enabled Analytics Develop, configure, test, and maintain student, advisor, and staff-facing AI agents using Microsoft Copilot Studio, Azure AI, Microsoft Fabric, and related technologies. Translate business use cases into technical requirements, solution designs, data requirements, prototypes, and testing plans. Build prompts, workflows, connectors, knowledge sources, integrations, and retrieval-augmented generation solutions using approved institutional data and documents. Monitor and troubleshoot enterprise solutions. Document AI architecture, data sources, testing results, safeguards, limitations, and responsible AI practices.   Data Quality, Classification, Metadata, and Lineage Develop and maintain data quality rules, validation processes, monitoring, and certification of trusted data assets and reports. Profile data and investigate errors; coordinate root-cause analysis and remediation. Apply and maintain data tags and approved definitions across enterprise platforms. Maintain metadata, automated harvesting, synchronization, and end-to-end lineage across systems.   Data Engineering and Operational Support Develop, test, monitor, and maintain data integration pipelines, Fabric assets, APIs, and related platform components using approved source-control, testing, deployment, security, and change-management practices. Onboard and integrate enterprise data sources, applications, files, APIs, and external services; build and validate extraction, transformation, reconciliation, loading, and automation processes. Document mappings, data flows, and operating procedures; troubleshoot pipeline failures, and performance issues.   Reporting and Analytics Develop and maintain Power BI dashboards, semantic models, analytical reports. Create dashboards that monitor AI performance, adoption, data quality, lineage, metadata, and pipeline reliability   Required Qualifications: Bachelor’s degree in computer science, information systems, data analytics, or a related field, or an equivalent combination of education and experience. At least 3-5 years of related experience in data integration, software development, analytics, automation, or reporting, and 2 years of experience with AI-enabled solutions. Working knowledge of SQL and experience with Python or another relevant programming or scripting language. Experience with one or more cloud data, integration,…