AI Engineer

• Lead the product strategy, roadmap, and delivery of AI-enabled solutions, translating complex business challenges into scalable products that drive measurable operational and customer outcomes.
• Define and optimize human-AI interaction models, including conversational experiences, prompt strategies, decision-support tools, and human-in-the-loop workflows that improve adoption, trust, and productivity.
• Partner closely with Engineering, Data Science, Operations, and Risk/Governance teams to prioritize opportunities, define requirements, and deliver AI products from concept through deployment and continuous optimization.
• Own product discovery and solution design by translating business needs into AI use cases, product requirements, user stories, success metrics, and implementation plans aligned to enterprise objectives.
• Balance innovation with governance by ensuring AI solutions adhere to enterprise controls, regulatory requirements, risk frameworks, and responsible AI principles within a highly regulated environment.
• Establish scalable product frameworks, reusable AI patterns, and best practices that accelerate AI transformation initiatives, modernize workflows, and unlock sustainable business value across the organization.
• Partner with oversight and governance contacts within ECRL as well as enterprise teams such as the COO Gen AI COE and Model Risk Management (MRM) on design validation, AI use case development, and model ownership.
• Serve as the user experience design lead on AI solutions, leaning on design thinking to establish fit for purpose delivery of technology solutions tied directly to operational goals.
• Partner with the product delivery team to ensure intent of design is maintained through delivery, testing is completed, and software development lifecycle (SDLC) and model development lifecycle (MDLC) requirements are met.
• Develop an integrated view of customer experiences across the Enterprise, ensuring interdependencies across organizations and functions are understood and map business capabilities to the business systems managed by IT to identify and resolve inconsistencies, duplications or gaps.
• Acts as a liaison between client area and technical organization by planning, conducting, and directing the analysis of current state and future state design.
• Conduct people, process, data, logic/ calculation and systems deep dive current state assessments to ensure all redundancies, deficiencies, failure points and constraints are understood and addressed.
• Recommend solutions from a people, process, business intelligence and technology perspective to standardize, streamline, and install appropriate control mechanisms for enhanced business performance, customer excellence, productivity, improved quality, etc.
• Conduct change impact analysis and develop capability gap closure roadmaps including short- and long-term implementation recommendations.
• Ensure target state alignment between the business model, operating model, organizational structure and IT solutions and capabilities.
• Consults with Enterprise Architects to ensure integration across domains.
• Identify risk through these transformations and evaluate and install appropriate control mechanisms to minimize risk.
• Perceives the impact and implications of decisions on other components of the company as well as impact on investors, customers and clients.
• Be an active voice to recommend solutions that will exploit opportunities and address critical deficiencies. Will also work closely with business lines and leadership across Wells Fargo in the execution of this strategy.

Senior Lead AI Transformation, Product Strategy

• Design, build, and deploy AI-powered features within our SaaS platform
• Integrate LLMs and cloud AI services into production applications
• Develop scalable backend services, REST APIs, and application integrations
• Build and maintain data pipelines that support AI workflows
• Contribute across the application stack, including backend services and frontend enhancements as needed
• Collaborate with Product Managers, UX Designers, and Software Engineers to deliver customer-centric solutions
• Rapidly prototype new AI capabilities and iterate on customer feedback
• Write clean, maintainable, well-tested code that follows software engineering best practices
• Optimize application performance, scalability, and reliability
• Participate in code reviews and contribute to architectural decisions

AI Software Engineer II

• Design, develop, and deploy machine learning models and AI-powered applications
• Collaborate with cross-functional teams to identify and implement AI/ML solutions for client needs
• Build and maintain data pipelines, model training workflows, and inference systems
• Evaluate and integrate large language models (LLMs), generative AI tools, and emerging AI frameworks
• Monitor model performance, conduct testing, and iterate on improvements
• Document technical architectures, methodologies, and best practices
• Stay current with AI/ML research and translate advancements into practical applications

• Define and evolve enterprise AI architecture, standards, reference models and implementation guidelines to support consistent solution delivery across business functions
• Align AI initiatives to enterprise strategies, roadmaps, and business priorities
• Identify and prioritize AI opportunities that drive student success and operational efficiency
• Design scalable AI platforms and solutions across data, application, and technology domains
• Provide guidance on how AI, ML, and automation will improve current and future-state architectures
• Lead design and implementation of AI-powered solutions (e.g., LLMs, agents, automation, and analytics)
• Integrate AI capabilities with enterprise applications and data platforms
• Guide evaluation and selection of AI/ML models, frameworks, and vendors
• Establish reusable patterns, APIs, and integration approaches
• Establish governance practices covering model oversight, responsible AI usage, security controls, compliance expectations, and lifecycle management
• Establish AI governance, including responsible AI, security, privacy, and compliance (FERPA, HIPAA, GDPR)
• Establish lifecycle management practices (MLOps/LLMOps, model monitoring, drift detection, retraining)
• Implement performance, bias, and risk monitoring frameworks
• Ensure secure AI architecture (e.g., data protection, isolation, adversarial defense)
• Partner with business, product, data, and engineering teams to provide AI solutions
• Provide architectural guidance to architects and delivery teams
• Facilitate workshops, POCs, demos and AI adoption projects identifying viable opportunities where AI can be a positive differentiator
• Mentor teams on AI best practices, patterns, and frameworks

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