Interested in this AI/ML Engineer role at Wells Fargo?
Apply Now →About This Role
Description
Title:AI‑fluent Product Designer
Location: Iselin, NJ
Duration: 6 months
Work Engagement: W2
Work Schedule: On site
Benefits on offer for this contract position: Health Insurance, Life insurance, 401K and Voluntary Benefits
Summary:
In this contingent resource assignment, you may: Consult as an expert to develop or influence initiatives and resources for highly complex business and technical needs across experience design, such as content design, design programs, design research and strategy, or product design. Consult on the strategy and resolution of highly complex and unique challenges requiring in\-depth evaluation across multiple areas, delivering solutions that are long\-term, large\-scale and require vision, creativity, innovation, and advanced analytical and inductive thinking. Provide expertise to client senior leadership on innovative experience design business solutions. Strategically engage with client personnel. Required Qualifications: experience design, content design, design programs, design research and strategy, or product design experience, or equivalent demonstrated through one or a combination of the following: work or consulting experience, training, military experience, education.
Key Responsibilities:
- Lead and execute User\-Centered Design (UCD) processes from discovery to delivery
- Translate complex functional requirements and user insights into simple, elegant, and accessible user interfaces built upon user flows, low\- to high\-fidelity wireframes, mockups, and interactive prototypes using Figma
- Align design decisions with business goals and user needs by strategically connecting or creating personas, experience journeys, and user flows
- Leverage AI\-assisted workflows where appropriate to improve speed and quality (e.g., prototyping acceleration and design\-to\-code collaboration)
- Collaborate within Agile/Scrum teams, participating in sprints, stand\-ups, planning sessions, and retrospectives
- Perform lightweight, early validation of designs and prototypes (e.g., time\-boxed UX reviews, quick usability checks and accessibility basics) to catch issues before development and reduce rework
- Advocate for consistent design system components, UI controls and patterns, and accessibility best practices
- Iterate designs based on user feedback and usability data to ensure the best possible experience
- Maintain clear and concise design documentation to support development handoff (interaction details, states, accessibility notes, and component usage guidance)
- Partner with engineering to design and ship reusable UI patterns/components; contribute improvements back to shared design system assets including component specs, states, and accessibility defaults
- Deliver core product design work while scaling reusable UI patterns/components and lightweight guidance (templates, examples, best practices) for broader adoption across multiple product engineering teams
- May oversee work performed by others and serve as a mentor
- Presents and defends designs and key milestone deliverables to peers and senior stakeholders
- Participates in roadmap planning and virtual teams; represents customer experience to our partners. Serves as SME for all aspects of customer experience.
Key Requirements:
- Applicants must be authorized to work for ANY employer in the U.S. This position is not eligible for visa sponsorship.
- UX design experience. Preference will be given to candidates who have experience designing complex solutions in diverse technology stacks.
- Proficiency in Figma for wireframing, prototyping, and design collaboration
- Solid understanding of User\-Centered Design (UCD) principles, methods, and tools
- Strong experience working in Agile environments, closely collaborating with cross\-functional teams
- Experience partnering closely with engineering on implementation details, including component behavior, states, and accessibility requirements for production\-ready handoff
- Demonstrated ability to create and maintain detailed user journey maps and user flows
- Familiarity with usability testing, accessibility (WCAG), and responsive design best practices
- Excellent communication skills, both verbal and visual – able to clearly articulate design decisions and rationale
- Experience with building and/or contributing to design systems
- Extensive experience in using and implementing UX design best practices to design solutions in large organizations
- Ability to work with stakeholders to understand strategic vision and detailed requirements and design complete user experiences that meet business goals and user needs
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 in Demand for This Role
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. Mid-level AI roles across all categories have a median of $200,000.
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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.