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About This Role
Why Wells Fargo
Are you looking for more? Find it here. At Wells Fargo, we're more than a financial services leader – we’re a global trailblazer committed to driving innovation, empowering communities, and helping our customers succeed. We believe that a meaningful career is much more than just a job – it’s about finding all of the elements to help you thrive, in one place.
Living the Well Life means you’re supported in life, not just work. It means having robust benefits, competitive compensation, and programs designed to help you find work\-life balance and well\-being. You’ll be rewarded for investing in your community, celebrated for being your authentic self, and empowered to grow. And we’re recognized for it – Wells Fargo once again ranked in the top three – making us the \#1 financial services employer – on the 2025 LinkedIn Top Companies list of best workplaces “to grow your career” in the U.S. Join us!
About this role:
Wells Fargo is seeking a Senior Lead AI Transformation/Product Strategy Leader for the Product Management and Delivery (PMD) team. This role plays an integral part in having an enterprise view of our customer concerns, identifying risks in a timely manner, providing enhanced reporting capabilities at the line of business and Enterprise levels, and delivering a variety of benefits to Wells Fargo Business.
In this role, you will:
- 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. Accordingly, critical success factors will include the ability to manage effectively in a matrix organization, develop partnerships with many businesses and functional areas, provide insight into the economic climate and related market developments, strong reputational risk and problem resolution and ability to identify trends which may present opportunities or reflect gaps in the operations.
Required Qualifications:
- 7\+ years of Product Management, product development, strategic planning, process management, change delivery, or agile product owner experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Desired Qualifications:
- Proven experience leading enterprise\-scale Generative AI and Agentic AI products, including autonomous workflows, reasoning systems, and tool\-integrated agents with measurable business outcomes.
- Demonstrated ability to design and implement AI operating models, including lifecycle frameworks, governance, and risk controls within highly regulated environments.
- Experience collaborating with governance and enterprise stakeholders (e.g., MRM, AI CoEs) to support AI solution validation, use case development, and model ownership.
- Experience leading UX design for AI\-driven solutions using design thinking to align with and advance operational goals.
- Experience partnering with product and engineering teams to maintain design integrity, oversee testing, and ensure adherence to SDLC/MDLC standards.
- Deep understanding of modern AI/ML architectures (LLMs, RAG, agent frameworks) combined with the ability to influence senior leadership and drive enterprise\-wide adoption.
- Experience applying structured business architecture practices to capture enterprise perspectives, guide strategy translation, and define high\-level solution design.
- Experience with setting business architecture direction and delivers strategic insights to design capabilities, processes, and operating models while simplifying complex environments.
- Experience with driving results through cross\-functional collaboration, critical thinking, and executive\-level insights to enable effective strategy execution and long\-term outcomes.
Job Expectations:
- This position is NOT eligible for Visa sponsorship.
- Ability to work on site per Wells Fargo's standard operating model in the listed location (hybrid schedule – 3 days on\-site \& 2 remote)
- The ability to travel up to 10% of the time.
Posting Location:
- Charlotte, NC
- Irving, TX
- Chandler, AZ
- Des Moines, IA
- San Antonio, TX
*The ECRL functions adhere to a location strategy; therefore, your candidacy may be determined based on your current location. Remote work locations are not available for these roles, so if you are not in a location listed on the posting, you must commit to self\-relocation within an agreed upon timeframe.*
Posting End Date:
20 Jul 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.
Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 3,708 AI roles we're tracking, AI/ML Engineer positions make up 70% of the market. At Wells Fargo, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $218,750 based on 3,817 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Wells Fargo AI Hiring
Wells Fargo has 13 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, AI Architect. Positions span Minneapolis, MN, US, Chandler, AZ, US, Charlotte, NC, US. Compensation range: $239K - $305K.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (102) are outnumbered by mid-level (1,705) and senior (1,469) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $300,000 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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