Principal Engineer - AI Enablement & Adoption

Wells Fargo•Iselin, NJ

2d•$159,000 - $305,000•Hybrid

About The Position

Wells Fargo is seeking a Principal Engineer, AI Enablement & Adoption to accelerate responsible, practical adoption of artificial intelligence across the organization. This highly visible role combines AI engineering, technical enablement, learning, and adoption. The Principal Engineer will build AI solutions and technical content, develop reusable resources and demonstrations, deliver hands-on training, and partner with teams to turn emerging AI capabilities into practical business outcomes. The role is responsible for defining and executing Technology’s AI learning and development strategy. The successful candidate will be a hands-on builder, educator, and technical influencer who can simplify complex AI concepts, create engaging learning experiences, and help teams progress from awareness to experimentation, implementation, and adoption. The position provides strategic technical leadership while remaining hands-on with AI technologies and solution development, and it offers a hybrid work schedule. The position is not eligible for visa sponsorship.

Requirements

At least 7 years of engineering experience, or equivalent demonstrated through work experience, training, military experience, education, or a combination of these.
At least 7 years of experience leading large-scale transformation, Learning & Development, Organizational Effectiveness, change management, adoption, and workforce enablement programs across multiple organizations.
At least 4 years of experience supporting Artificial Intelligence, Generative AI, data, or emerging technology initiatives.

Nice To Haves

Experience designing, developing, or implementing Generative AI, Agentic AI, intelligent automation, or other emerging technology solutions.
Experience creating and delivering technical learning content, workshops, demonstrations, hackathons, enablement programs, or hands-on training.
Hands-on programming or solution development experience using Python, Java, C#, C++, or comparable programming languages.
Experience with AI agents, prompt and tool design, retrieval-augmented generation (RAG), orchestration, model evaluation, observability, and human-in-the-loop solutions.
Experience with enterprise AI and automation platforms such as Microsoft AI technologies, Copilot Studio, Power Automate, UiPath, or comparable platforms.
Experience developing proofs of concept, prototypes, reusable technical assets, reference implementations, and solution demonstrations.
Experience supporting technology adoption through technical coaching, enablement, implementation guidance, and change-focused activities.
Experience evaluating emerging technologies and translating findings into practical engineering or adoption recommendations.
Knowledge of responsible AI practices, including security, data protection, access management, testing, auditability, governance, and human oversight.
Experience collaborating across engineering, architecture, product, business, risk, cybersecurity, and governance organizations.
Strong presentation, facilitation, technical storytelling, and stakeholder communication skills.
Demonstrated ability to influence technical direction and adoption while remaining hands-on with solution development.

Responsibilities

Design, prototype, and build AI, Generative AI, Agentic AI, automation, and intelligent workflow solutions that demonstrate practical business applications.
Develop AI technical content, demonstrations, learning experiences, reference implementations, playbooks, use cases, labs, templates, and implementation guidance.
Design and deliver workshops, technical demonstrations, hands-on training, and enablement sessions for technical and business audiences.
Partner with teams to identify AI opportunities and translate business needs into practical AI-enabled solutions and workflows.
Create reusable AI assets, including agents, prompts, workflows, prototypes, technical examples, and implementation patterns.
Coach teams through the AI adoption lifecycle, from exploring capabilities to building, testing, refining, and incorporating AI into existing ways of working.
Translate emerging AI technologies into actionable guidance covering capabilities, limitations, implementation considerations, and appropriate use cases.
Evaluate emerging AI technologies, platforms, and frameworks and recommend approaches based on business value, technical requirements, risk, and scalability.
Incorporate security, data protection, access controls, testing, evaluation, observability, governance, and human oversight into AI solutions and enablement materials.
Develop reusable standards, best practices, and lessons learned to help scale successful AI approaches across teams.
Partner with engineering, architecture, product, cybersecurity, risk, governance, and business stakeholders to enable responsible AI implementation.
Advise and advocate for AI adoption, helping leaders and teams understand emerging capabilities, technical tradeoffs, and implementation opportunities.
Mentor engineers and practitioners and contribute to a culture of experimentation, continuous learning, innovation, and responsible AI adoption.
Provide strategic technical leadership while maintaining hands-on involvement in AI technologies and solution development.
Maintain awareness of emerging AI technologies and translate relevant capabilities into practical enterprise applications.