Artificial Intelligence Solutions Transformation Analyst

***We are unable to provide sponsorship for this permanent full-time role in Chicago***

***Position is bonus eligible***

Prestigious Financial Institution is currently seeking a Artificial Intelligence Solutions Transformation Analyst. Candidate will be the primary interface between the business and its AI transformation agenda. Rather than waiting for use cases to arrive, the AI Solutions Lead embeds with assigned business functions to understand how work gets done, identify where AI creates meaningful opportunity, and build the roadmap that sequences those opportunities from quick wins through to AI-native operating models. 

Responsibilities:

AI Opportunity Identification & Transformation Roadmap 

• Embed with assigned business functions to develop a grounded understanding of how workflows, where decisions are made, and where AI can create the most meaningful change in how the organization operates. 
• Identify AI opportunities that redesign how work gets done rather than layer automation on top of existing processes; distinguish between use cases that deliver incremental improvement and those that change the operating model. 
• Build and maintain the AI transformation roadmap for assigned functions: sequencing initiatives by impact, feasibility, and readiness, from immediate quick wins through to longer-term AI-native transformation. 
• Partner with functional leaders to build a shared, realistic view of what AI-native operations look like in their area and what it takes to get there. 

Use Case Delivery 

• Own the full lifecycle of assigned use cases: problem definition, stakeholder requirements, scoping, acceptance criteria, delivery oversight, and post-launch adoption. 
• Translate business problems into technically grounded requirements precise enough that the CTO’s engineering team can begin architecture design without a separate discovery phase. 
• Own the primary relationship between the business unit and the CTO’s engineering team for assigned initiatives, translating clearly in both directions. 
• Define the definition of done: what gets built must solve the problem it was designed to solve. 
• Design and execute adoption plans for assigned use cases: track usage post-launch, identify friction, and drive resolution until users are genuinely working with the solution. 
• Communicate roadmap, performance, and trade-offs to business stakeholders and leadership for assigned use cases. 
• Escalate enterprise-level adoption patterns and systemic barriers to the AI Enablement & Change Lead. 
• Contribute to portfolio reviews with current status, outcome data, and risk flags for assigned use cases. 

Technical Evaluation & Advisory 

• Assess the technical feasibility of proposed use cases before they enter any pipeline: what is executable, what data prerequisites exist, and what the realistic path to production looks like. 
• Lead vendor and tool evaluations for non-centralized use cases, producing build-vs-buy recommendations defensible to the business, the CTO, and ORM. 

Citizen-Led AI Development 

• Support business units pursuing contained, non-critical AI development or POCs where the CTO team has assessed the initiative as outside the centralized pipeline; provide technical guidance, scoping, and governance guardrails so work proceeds within approved framework. 
• Support organizational AI capability development across functions: help teams understand what AI tools can do in their specific context and how to use them well. 
• Define clear boundaries for each citizen-led initiative: what can proceed with guided self-service, what needs central engineering involvement, and what requires full Working Group oversight. 

Qualifications:

• Deep understanding of financial services or capital markets operations; experience in post-trade, clearing, custody, or comparable financial market infrastructure strongly preferred. 
• Track record of operating at the intersection of business strategy and technical delivery: has produced specifications that engineering teams have built from, and business cases that senior leaders have approved. 
• Experience identifying technology opportunities inside a business — proactively finding where AI creates value, not just responding to requests. 
• Working knowledge of modern AI architectures, including retrieval-augmented generation, agent frameworks, and large language model evaluation, sufficient to assess vendor claims, scope POCs, and produce requirements engineering teams can act on. 
• Demonstrated ability to drive post-launch technology adoption: accountable through genuine usage, not just go-live. 
• Familiarity with model risk governance expectations and their implications for AI use in a regulated financial environment. 

• AI architecture assessment: retrieval-augmented generation, agent frameworks, and large language model evaluation 
• Requirements specification for AI systems and use case feasibility analysis 
• Vendor and tool evaluation methodology for AI platforms in regulated environments 
• Data analysis and familiarity with data pipeline concepts relevant to AI use cases 
• Process analysis and workflow mapping to support use case scoping and AI readiness work 
• Analytics and BI tools (e.g., Power BI, Tableau) and collaboration platforms (e.g., Jira, Notion) 

• 7 or more years of experience in financial services technology or strategy, with at least five years in post-trade or capital markets infrastructure 
• Domain knowledge in at least one of the following: tokenization and digital asset infrastructure, collateral management and margining, or extended-hours clearing and settlement operations 

• Relevant AI product management, project management, or domain certification is a plus