AI Solutions Engineering & Transformation Manager

Role Title - Manager – AI Solutions Engineering & Transformation About Morningstar Morningstar, Inc. is a leading provider of independent investment insights in North America, Europe, Australia, and Asia. The Company offers an extensive line of products and services for individual investors, financial advisors, asset managers and owners, retirement plan providers and sponsors, institutional investors in the debt and private capital markets, and alliances and redistributors. Morningstar provides data and research insights on a wide range of investment offerings, including managed investment products, publicly listed companies, private capital markets, debt securities, and real-time global market data. Role Context India GIH is scaling AI adoption across business, operations, research, technology and enabling functions. The organization already has AI awareness, AI literacy, AI Champion and showcase mechanisms in place. The next stage of maturity requires stronger execution support: teams need help converting use-case ideas into practical solution designs, prototypes, MVPs and production-ready implementations. This need is especially acute for smaller or non-technology teams that understand their workflows but do not have dedicated engineering or AI solution-design capacity. This role is intended to fill that gap. The Manager – AI Solutions Engineering and Transformation will act as an internal AI advisor, solution architect, prototype, and solution developer & delivery catalyst. The role will not merely evangelize AI or run training sessions; it will help teams make concrete progress from idea identification to impact realization. The manager will bring consulting discipline, technical fluency, hands-on experimentation, stakeholder management and responsible AI governance into one integrated role. Roles and Responsibilities A. AI Advisory and Business Problem Framing Partner with functional leaders, managers and AI Champions to identify workflow pain points, productivity opportunities, quality challenges, turnaround-time bottlenecks and client-experience improvement areas that could benefit from AI. Challenge vague AI ideas and convert them into outcome-oriented use cases with clear scope, and measurable value Help business sponsors articulate success measures such as hours saved, effort reduction, throughput improvement, accuracy improvement, faster cycle time, reduced rework or improved user satisfaction. B. Solution Design and Architecture Advisory Translate business requirements into practical AI solution designs, including user flow, data flow, model/tool choice, integration points, controls, monitoring needs and expected operational ownership. Recommend the appropriate solution path across multi-stack options such as Microsoft Copilot / Copilot Studio, Azure OpenAI, OpenAI APIs, AWS Bedrock, internal platforms, vendor tools, workflow automation, OCR/document AI, data pipelines or traditional automation.