below marketmost similar roles pay hereabove market
This role pays more than
70%
of similar roles. Most pay
$185,375–$254,275
— the blue band above.
At the midpoint, this role pays about
$232k
versus about
$220k
for comparable roles.
Based on 240 similar postings.
Employer
About Wells Fargo
Wells Fargo & Company is one of the largest banks in the United States, providing banking, investment, mortgage, and consumer and commercial finance products and services nationwide. Industry: Banking & Financial Services
Wells Fargo currently has
37 open roles
on FindRole.
Listed pay typically runs
$139,000–$239,000
across 16 roles with salary data.
JOB TITLE: Principal Engineer - AI Enablement & Adoption
The Principal Engineer - AI Enablement & Adoption joins the team to accelerate the responsible and practical adoption of artificial intelligence across the organization. This role involves designing, prototyping, and building AI, Generative AI, Agentic AI, automation, and intelligent workflow solutions. The individual will develop technical content, including demonstrations, playbooks, and learning experiences, while delivering workshops and hands-on training for technical and business audiences. Key responsibilities include creating reusable assets like agents, prompts, and RAG implementations, and establishing standards for security, data protection, and governance. Required skills include experience with Python, Java, C#, C++, and platforms like Microsoft AI technologies, Copilot Studio, and Power Automate. The role solves the business problem of translating emerging AI capabilities into practical, scalable, and secure enterprise applications.
What does a Engineer earn in Texas?
Median $189760 from 40 postings across 19 companies.
Design, prototype, and build AI, Generative AI, Agentic AI, and intelligent workflow solutions for business applications.
Develop technical content including demonstrations, learning experiences, playbooks, use cases, and implementation guidance.
Design and deliver workshops, technical demonstrations, and hands-on training for technical and business audiences.
Partner with teams to identify AI opportunities and translate business needs into practical AI-enabled workflows.
Create reusable AI assets such as agents, prompts, prototypes, and technical examples to accelerate adoption.
Coach teams through the AI adoption lifecycle to explore capabilities, build solutions, and refine approaches.
Evaluate emerging AI technologies and frameworks based on business value, technical requirements, risk, and scalability.
Incorporate security, data protection, governance, and human oversight into all AI solutions and enablement materials.
What we're looking for
7+ years of Engineering experience or equivalent demonstrated through work, training, military experience, or education.
7+ years of experience leading large-scale transformation, Learning & Development, Organizational Effectiveness, change management, adoption, or workforce enablement programs.
4 years of experience supporting Artificial Intelligence (AI), Generative AI (GenAI), data, or emerging technology initiatives.
Experience designing, developing, or implementing Generative AI, Agentic AI, intelligent automation, or other emerging technology solutions (preferred).
Experience creating and delivering technical learning content, workshops, demonstrations, hackathons, enablement programs, or hands-on training (preferred).
Hands-on programming or solution development experience using Python, Java, C#, C++, or comparable programming languages (preferred).
Experience with AI agents, prompt and tool design, retrieval-augmented generation (RAG), orchestration, model evaluation, observability, and human-in-the-loop solutions (preferred).
Experience with enterprise AI and automation platforms such as Microsoft AI technologies, Copilot Studio, Power Automate, UiPath, or comparable platforms (preferred).
Generative AI
Python
RAG
LLMs
Vector Databases
Agentic Workflows
Prompt Engineering
Distributed Systems
API
cloud‑native architecture
Model Orchestration
Observability
Performance Optimization
Data Ingestion
Semantic Search
Machine Learning
Deep Learning
Python
TensorFlow
PyTorch
Natural Language Processing
Computer Vision
Java
C++
Git
Agile
Data Mining
Data Modeling
Statistical Modeling
Distributed Computing
Neural Networks
CNN
RNN
GAN