AI Solutions Analyst

Job Summary At Vantage Bank, we are driven by a deep commitment to supporting our customers, valuing our employees, embracing diversity, fostering meaningful connections, and providing outstanding service every step of the way. The AI Solutions Analyst supports the design, validation, deployment, and continuous improvement of governed AI-enabled analytics solutions across the bank. This role translates business problems into practical natural-language analytics experiences, AI/BI dashboards, SQL AI enrichment workflows, and agentic analytics prototypes within the Data Intelligence Platform. Working with Analytics, Data Engineering, Data & AI Governance, Compliance, and Software Development teams, the position helps prepare trusted data assets, test AI behavior, document solution logic, and support responsible AI adoption. This role provides a specialist growth path for employees developing expertise in applied AI, analytics solution design, validation, and governed AI adoption. Essential Duties Design, prototype, and support governed natural-language analytics experiences, including Genie Spaces, AI/BI dashboard Q&A, and agent-assisted query workflows. Prepare trusted data assets, semantic definitions, verified SQL examples, and Unity Catalog metadata for AI-assisted analytics use cases. Translate business questions into decision logic, metric definitions, prompt instructions, verified queries, test cases, and acceptance criteria. Maintain lineage, catalog tagging, governance assumptions, and input/output documentation to support auditability and solution reliability. Evaluate AI tools and frameworks, including Mosaic AI, AI Gateway, Genie, Databricks Apps, Lakehouse Apps, Azure OpenAI, OpenAI, LangChain, or comparable technologies. Design validation and testing procedures for AI-generated outputs, including answer accuracy, hallucination risk, access boundaries, regression testing, and human review points. Create documentation, enablement materials, adoption guides, and safe-use guidance for business users adopting AI-assisted analytics workflows. Support workflows that use prompt libraries, approved-model registries, audit logging, usage monitoring, cost attribution, and approval routing for higher-risk AI use cases. Support SQL AI Functions and AI enrichment workflows for summarization, classification, extraction, and structured output generation; validate generated fields before downstream use. Participate in cross-functional design sessions to scope agentic analytics experiences, define deployment readiness criteria, and document business acceptance requirements. Apply privacy, compliance, responsible AI, and model risk considerations when designing or testing AI-assisted solutions. Qualifications Required: Bachelor’s degree in Data Science, Computer Science, Information Systems, Statistics, Business Analytics, or a related quantitative field; equivalent applied experience may be considered. 3+ years of experience in analytics, data engineering, AI solution delivery, business intelligence, or data platform enablement in a cloud-native environment. Understanding of the AI solution lifecycle, including use case intake, data readiness, prompt and instruction design, validation planning, deployment readiness, adoption measurement, and continuous improvement. Strong SQL proficiency and working Python or scripting knowledge to inspect data, validate AI outputs, automate tests, and support lightweight prototypes. Experience with LLM tools, natural-language analytics, chatbot experiences, retrieval-augmented generation, or agent frameworks such as Databricks Genie, Mosaic AI, Azure OpenAI, OpenAI, LangChain, or comparable technologies. Familiarity with Unity Catalog, Atlan, or comparable metadata and governance platforms, including catalog navigation, lineage review, tagging, classification, and glossary alignment. Experience designing validation and testing workflows for AI-assisted outputs, including benchmark datasets, expected‑answer comparisons, hallucination checks, access‑boundary testing, and regression testing. Practical understanding of Databricks AI/BI capabilities, Genie Spaces, Unity Catalog business semantics, and governed data access patterns for self‑service analytics. Ability to document solution logic, prompt instructions, verified queries, test cases, governance assumptions, limitations, and user adoption guidance in an audit‑ready format. Strong problem‑solving, facilitation, communication, and translation skills across technical teams, business stakeholders, governance partners, and non‑technical users. Preferred: Experience curating Genie Spaces, natural‑language analytics experiences, or comparable conversational BI interfaces for governed business consumption. Familiarity with AI Gateway or comparable LLM governance capabilities, including approved‑model routing, usage monitoring, audit logging, cost attribution, and rate limits. Experience with Azure DevOps, GitHub, backlog prioritization, agile delivery, test case tracking, and release readiness documentation. Experience with SQL AI Functions, Databricks Apps, Lakehouse Apps, Streamlit, Gradio, or comparable lightweight application frameworks. Working awareness of compound AI, agentic analytics, retrieval‑augmented generation, tool‑calling workflows, and human‑in‑the‑loop review patterns. Exposure to responsible AI practices in financial services, including acceptable use boundaries, model risk awareness, explainability expectations, and audit‑ready documentation. Understanding of banking or financial services data, including customer, deposit, lending, fraud, AML, compliance, or operational reporting domains. Equal Opportunity Employer Statement We are an Equal Opportunity Employer. M/F/D/V. #J-18808-Ljbffr Vantage Bank