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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 highly strategic and execution-oriented Senior Lead Artificial Intelligence Consultant to help shape the future of AI-powered transformation across the enterprise. As part of the Chief Operating Office (COO) Artificial Intelligence Center of Excellence (AI COE), this role will identify, design, and deliver innovative Generative AI and Agentic AI solutions that drive business efficiency, enhance customer and employee experiences, and create measurable operational impact.
This is a technical-functional leadership role that requires a unique blend of deep AI expertise, business process transformation experience, and executive-level communication skills. The successful candidate will serve as a trusted advisor to senior leaders, translating complex technical concepts into clear business outcomes while partnering with stakeholders across technology, operations, risk, control, and business organizations.
In this role, you will:
- The Senior Lead AI Consultant will work closely with business and technology teams to uncover high-value AI opportunities, define solution strategies, and lead the end-to-end delivery of AI-enabled capabilities. This role requires the ability to bridge technical implementation with business execution, ensuring solutions are scalable, responsible, and aligned with enterprise priorities.
- Success in this position requires strong expertise in AI technologies, product and solution development, business process optimization, stakeholder management, and the ability to influence and communicate effectively with audiences ranging from engineers and data scientists to executive leadership and senior business partners.
- Partner directly with business and operations leaders to identify high-value opportunities for Generative AI, Agentic AI, and intelligent automation.
- Analyze business processes, user workflows, data, and operational pain points to determine where AI can deliver measurable value.
- Assess opportunities for feasibility, risk, scalability, and reuse, and develop business cases that quantify efficiency, cost, quality, risk, customer experience, and SLA improvements.
- Translate business needs into clear AI solution concepts, requirements, success measures, and implementation roadmaps.
- Lead the end-to-end design of AI and agentic solutions, including workflow orchestration, data integration, human-in-the-loop controls, reusable tools, and enterprise system connectivity.
- Work alongside engineering, architecture, data, product, risk, and operations teams to rapidly prototype, build, and refine solutions based on user feedback and operational results.
- Provide hands-on support during development by resolving requirements, design, integration, and implementation challenges.
- Define and coordinate testing across functional performance, AI quality, controls, user acceptance, resiliency, and production readiness.
- Guide solutions through applicable AI, model, technology, privacy, security, and change-management requirements.
- Lead deployment into business workflows, including operational readiness, user adoption, documentation, training, support models, and transition to production ownership.
- Monitor solution performance and business outcomes after deployment, identify improvement opportunities, and drive ongoing optimization.
- Develop reusable agent skills, tools, integrations, solution patterns, and delivery playbooks that accelerate future implementations and reduce duplication.
- Manage the organization’s Agentic AI roadmap, including opportunity intake, prioritization, sequencing, dependencies, investment needs, and delivery milestones.
- Collaborate with business and technology leaders to align the Agentic AI roadmap with broader product, data, automation, and operational-transformation strategies.
- Serve as the senior technical and functional advisor for AI solution delivery, communicating progress, risks, investment needs, and realized value to executive stakeholders.
- Lead and mentor forward deployed engineers and cross-functional delivery teams, establishing consistent standards for solution quality, responsible AI, execution discipline, and measurable business impact.
Required Qualifications:
- 7+ years of Artificial Intelligence Solutions experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
Desired Qualifications:
AI & Agentic Engineering: Proven experience leading enterprise-scale Generative AI and Agentic AI products, including autonomous workflows, reasoning systems, and tool-integrated agents with measurable business outcomes.
Enterprise AI Strategy, Operating Model & Governance: Demonstrated ability to design and implement AI operating models, including lifecycle frameworks, governance, and risk controls within highly regulated environments.
Analytics, Data Engineering, Process Intelligence & Transformation: Experience leveraging task mining, behavioral analytics, or digital twin platforms (e.g., SKAN.ai) to drive automation, agentic workflows, and productivity gains.
Multi-Year Roadmap & Transformation Delivery: Track record of defining and executing enterprise AI product roadmaps, aligning cross-functional teams across technology, data, AI/ML, and operations.
Delivery Leadership & Executive Liaison: Proven ability to drive end-to-end product delivery while serving as a strategic liaison between business stakeholders, technology teams, and executive leadership to ensure alignment, transparency, and successful outcomes.
Business Consulting: Strong ability to quantify and communicate ROI for AI investments, including efficiency, SLA, accuracy, and cost improvements at scale.
Solution Architecture: Deep understanding of modern AI/ML architectures (LLMs, RAG, agent frameworks) combined with the ability to influence senior leadership and drive enterprise-wide adoption.
Executive Communication & Consulting Skills: Demonstrated ability to serve as a trusted advisor to executive leadership, translating complex technical and AI concepts into clear, compelling business strategies and outcomes. Proven experience influencing senior stakeholders, leading executive-level discussions, and building consensus across technology, operations, risk, control, and business organizations to drive enterprise-wide transformation initiatives.
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, NC
* The Global Operations 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:
21 Oct 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 9,079 AI roles we're tracking, AI/ML Engineer positions make up 76% 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 $211,950 based on 13,048 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $229,300.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Wells Fargo AI Hiring
Wells Fargo has 11 open AI roles right now. They're hiring across AI/ML Engineer, Research Scientist, AI Engineering Manager, AI Software Engineer. Positions span West Des Moines, IA, US, St. Louis, MO, US, Charlotte, NC, US. Compensation range: $260K - $305K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 roles, $210,000 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, and the 90th percentile reaches $318,400. 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 $256,000 median, while Prompt Engineer roles sit at $141,440. 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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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