AI Enterprise Enablement Leader

Wells Fargo & Company•Irving, TX

2d•Hybrid

About The Position

Wells Fargo is seeking a strategic and visionary leader to drive enterprise strategy and architecture for AI enablement, metadata management, semantic layer capabilities, and the enterprise information marketplace. This role will sit within the Chief Data Office (CDO) and is responsible for mobilizing and delivering priority initiatives. This leader will play a critical role in defining and executing AI-enabled data strategies while aligning cross-functional stakeholders across Data Governance, Technology Tooling, Cyber (SecAI migration), Privacy, and delivery teams. The role requires a blend of deep technical expertise, strategic leadership, and execution discipline to deliver scalable, secure, and innovative AI solutions in a highly regulated financial services environment.

Requirements

10+ years of technology strategic leadership experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
4+ years of management or leadership experience
7+ years of engineering experience.
7+ years of stakeholder management and cross-functional collaboration.
4+ years working with Agile methodologies (Scrum, SAFe, Kanban).
4+ years of experience in financial services or highly regulated industries.
3+ years of experience in data engineering and/or data management with hands-on solution delivery.
1+ year of experience with generative AI, including retrieval-augmented generation (RAG) and agentic workflows.

Nice To Haves

Deep expertise in modern AI architectures, machine learning, and generative AI technologies.
Experience with LangChain and exposure to LangGraph or similar orchestration frameworks.
Experience building RAG pipelines and working with vector databases.
Familiarity with cloud platforms (Vertex AI preferred) and modern backend/frontend stacks.
Experience with Docker, Kubernetes, CI/CD, and GenAI evaluation/observability.
Knowledge of API security, PII handling, and secrets management.
Strong understanding of enterprise data management, metadata systems, and semantic architectures.
Proven ability to design and implement AI systems leveraging large language models (LLMs) with a focus on safety, accuracy, and usability.
Exceptional strategic thinking with the ability to integrate AI innovation into enterprise architecture.
Strong leadership and decision-making skills with experience driving large-scale initiatives.
Excellent communication skills with the ability to influence executive stakeholders.
Demonstrated ability to manage risk, compliance, and ethical considerations in AI deployments.
Experience with enterprise AI governance frameworks (Model Risk Management, data policies, review processes).
Results-driven mindset with a focus on measurable business outcomes.

Responsibilities

Define and execute the CDO AI strategy for engaging with enterprise AI teams to increase adoption and usage of approved data sources
Partner with CDO leaders and Data Domains to create and execute a clear AI strategy and delivery of reusable AI components to accelerate the work of data organizations across the enterprise, ensuring alignment with enterprise priorities and business outcomes.
Establish and operationalize a hub-and-spoke model for AI enablement, where centralized capabilities (hub) support scalable, domain-driven innovation (spokes).
Act as a visionary leader to position the organization at the forefront of agentic AI and next-generation data capabilities.
Lead organizational transformation initiatives promoting AI adoption and data-driven decision-making.
Drive consistency in AI architecture, tooling, and governance while enabling flexibility for domain-specific use cases and innovation.
Identify cross-domain opportunities to accelerate reuse of data, models, and AI solutions through shared platforms and capabilities.
Oversee the end-to-end lifecycle of AI solutions, including planning, development, deployment, and continuous improvement.
Develop multi-step workflows using LangChain and LangGraph (chains, tools, retries, error handling).
Implement prompts, tool integrations, memory patterns, and basic observability (logging, tracing, guardrails).
Build ingestion pipelines (parsing, chunking, embeddings, metadata tagging).
Implement retrieval strategies (dense, hybrid, reranking) and manage vector databases (Pinecone, Weaviate, FAISS).
Support domain adaptation via schema mapping and structured data integration.
Ensure all AI solutions meet enterprise standards for security, risk, compliance, and non-functional requirements.
Champion innovative AI practices including generative AI, retrieval-augmented generation (RAG), and agentic workflows.
Ensure solutions support application modernization, cloud readiness, and enterprise scalability.
Collaborate with enterprise architects and senior engineers to align with broader technology strategies and domain architectures.
Develop and deploy services using Vertex AI (or equivalent), including endpoints and pipelines.
Support containerization (Docker), Kubernetes deployment, and CI/CD workflows.
Build backend APIs (Python/Node.js) and support frontend components (React/Next.js).
Implement authentication, authorization, and API management controls.
Drive seamless migration paths to next-generation AI and data platforms.
Maintain deep expertise in AI/ML, deep learning, and emerging technologies to guide architectural decisions.
Establish and enforce best practices for AI model development, deployment, monitoring, and lifecycle management.
Lead and manage complex, cross-functional AI initiatives from ideation through execution and production deployment.
Implement GenAI evaluation pipelines (relevance, latency, cost, accuracy).
Use evaluation tools (RAGAS, G-Eval) and monitor model performance for improvements.
Identify and resolve technology and operational barriers across the software development lifecycle.
Govern AI delivery processes including model risk management, data usage controls, and architecture/design reviews.
Track and measure business impact and ensure delivery of tangible outcomes aligned with enterprise objectives.
Influence and partner with CDO executive leadership, enterprise AI teams, and broader technology organizations.
Collaborate with external vendors and service providers to ensure solution quality and strategic alignment.
Communicate complex technical concepts clearly to both technical and non-technical stakeholders.
Partner with product, data, and platform teams to deliver AI features.
Promote engineering best practices and adoption of emerging GenAI tools and frameworks.
Build strong partnerships with Data Domain leaders to understand domain-specific needs and translate them into scalable AI and data solutions.
Act as a central point of coordination between the CDO, domain teams, and enterprise AI partners to ensure cohesive execution of AI initiatives.
Enable and guide domains in adopting enterprise AI capabilities, including metadata, semantic layer, and information marketplace services.
Facilitate collaboration across domains to promote reuse, reduce duplication, and drive standardization of AI and data practices.
Support domains in prioritizing AI use cases, ensuring alignment with governance, risk, and enterprise architecture standards.
Build, lead, and mentor a high-performing AI and data engineering team.
Drive recruitment, training, and professional development initiatives.
Foster a culture of innovation, accountability, and continuous learning.