About this role:
Wells Fargo is seeking a strategic and visionary leader to drive enterprise strategy and architecture for AI enablement. This role will sit within the Chief Data Office (CDO) and is responsible for partnering to drive adoption of Approved Data Sources in enterprise AI use cases, ensuring efficient scalability and reuse of AI Data capabilities across the firm in partnership with enterprise data domains, and delivering priority CDO AI initiatives.
This leader will play a critical role in defining and executing AI-enabled data strategies while aligning cross-functional stakeholders across the enterprise and data domains. 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.
In this role, you will:
Strategy & Leadership
- 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 data enablement, where centralized capabilities (CDO 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.
AI & Technology Delivery
- 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.
Architecture & Engineering
- 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.
Execution & Delivery
- 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.
- Champion and adhere to responsible 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.
Stakeholder Management & Collaboration
- Influence and partner with CDO leaders, 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.
People Leadership
- 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.
Required Qualifications:
- 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 leadership or management 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.
Desired Qualifications:
- 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.
Job Expectations:
Ability to work at a designated Wells Fargo location in accordance with the enterprise hybrid work model.
Willingness to travel up to 10-15% as needed to collaborate with cross-functional teams and stakeholders.
Posting End Date:
5 Jun 2026
*Job posting may come down early due to volume of applicants.
We Value Equal Opportunity
Wells Fargo is an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other legally protected characteristic.
Employees support our focus on building strong customer relationships balanced with a strong risk mitigating and compliance-driven culture which firmly establishes those disciplines as critical to the success of our customers and company. They are accountable for execution of all applicable risk programs (Credit, Market, Financial Crimes, Operational, Regulatory Compliance), which includes effectively following and adhering to applicable Wells Fargo policies and procedures, appropriately fulfilling risk and compliance obligations, timely and effective escalation and remediation of issues, and making sound risk decisions. There is emphasis on proactive monitoring, governance, risk identification and escalation, as well as making sound risk decisions commensurate with the business unit’s risk appetite and all risk and compliance program requirements.
Candidates applying to job openings posted in Canada: Applications for employment are encouraged from all qualified candidates, including women, persons with disabilities, aboriginal peoples and visible minorities. Accommodation for applicants with disabilities is available upon request in connection with the recruitment process.
Applicants with Disabilities
To request a medical accommodation during the application or interview process, visit Disability Inclusion at Wells Fargo.
Drug and Alcohol Policy
Wells Fargo maintains a drug free workplace. Please see our Drug and Alcohol Policy to learn more.
Wells Fargo Recruitment and Hiring Requirements:
a. Third-Party recordings are prohibited unless authorized by Wells Fargo.
b. Wells Fargo requires you to directly represent your own experiences during the recruiting and hiring process.
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