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About This Role
Wells Fargo is seeking a Lead AI Java Software Engineer to join our Prime Services technology team to be part of Risk and Margin platform transformation initiative leveraging GenAI to accelerate development and build new capabilities. In this role as an AI engineer, you will develop cutting edge artificial intelligence solutions using large language models, agentic frameworks, and other innovative technologies. The ideal candidate would have knowledge in designing, developing, and deploying applications leveraging LLM models and systems, with a strong focus on Prompt Engineering, Fine Tuning, RAG implementation, and Agentic frameworks.
The Risk and Margin suite is built on common foundational frameworks and is used to deliver sophisticated and complex front office solutions. This person will be actively engaged in architecture, design, development, unit testing, and stress testing of a high\-performance framework for applications and be a key participant in a highly talented delivery team for front\-office systems and tools development. Additionally, they will be expected to provide hands on technical support, advice, and consultation with open issues relating to supported applications plus ensure quality, performance, security, trading risk controls, and compliance for those application platforms.
The ideal candidate will be an expert in solving software, and infrastructure problems in relation to complex processes. They will have experience with designing and developing automated testing harnesses, recommending, and implementing solutions, participating in and conducting code review sessions, and providing process improvements. Past experience in building Risk and Margin methodologies is needed. We are keenly interested in high energy people with a passion to build common core high performance technology to be used across asset classes in Equities and Fixed Income.
Learn more about the career areas and business divisions at wellsfargojobs.com.
In this role, you will:
- Lead complex initiatives on selected domains
- Ensure systems are monitored to increase operational efficiency and managed to mitigate risk
- Define opportunities to maximize resource utilization and improve processes while reducing cost
- Lead, design, develop, test and implement applications and system components, tools and utilities, models, simulation, and analytics to manage complex business functions using sophisticated technologies
- Resolve coding, testing and escalated platform issues of a technically challenging nature
- Lead team to ensure compliance and risk management requirements for supported area are met and work with other stakeholders to implement key risk initiatives
- Mentor less experienced software engineers
- Collaborate and influence all levels of professionals including managers
- Lead team to achieve objectives
- Partner with production support and platform engineering teams effectively
AI‑Enabled Engineering and Platform Responsibilities
- Define and govern standard patterns for AI‑assisted and LLM‑based software development, including prompt‑driven development, automated test generation, diagnostics, documentation, and developer productivity tooling
- Architect and guide enterprise‑standard LLM integrations, including MCP‑based services, used across development tooling, system observability, and controlled automation
- Establish and enforce guardrails for reliable, explainable, auditable, and secure AI usage within electronic trading technology
- Partner with platform, security, risk, and compliance teams to ensure AI‑enabled engineering workflows meet Wells Fargo standards for data protection, regulatory compliance, and model risk management
- Continuously evaluate advances in GenAI, agentic AI, and applied machine learning to refine engineering practices and platform capabilities where appropriate
*(AI‑enabled development is considered a baseline capability across the engineering organization; this role ensures architectural consistency, operational safety, and enterprise scale.)*
Required Qualifications:
- 5\+ years of Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- 5\+ years of experience in building prime brokerage or capital markets applications
- 5\+ years of high\-performance programming techniques in Java
- 5\+ years of experience in Java, Spring Boot, JPA, Microservices, SQL and NoSQL
- 2\+ years of experience in Kubernetes/OpenShift \& cloud technologies
- 5\+ years of experience in Apache Kafka/Solace/MQ
- 5\+ years of Experience with SDLC and Agile tools such as JIRA, GitHub, Jenkins, Confluence etc.
- 2\+years of experience on GenAI enabled solutions, Retrieval\-Augmented Generation (RAG) and agentic workflows, with a strong understanding of prompt engineering, grounding, and extensibility
- 2\+years hands\-on experience with major GenAI platforms (e.g., Microsoft VS Code, GitHub Copilot)
Desired Qualifications:
- 2\+ years of experience in building risk \& margin with Portfolio margin, Rules based margin calculations and stress testing
- SME on equities, options and fixed income securities.
- Knowledge of core computer science design concepts, algorithms, and data structures
- Familiarity with Model Context Protocol (MCP), enterprise\-grade agentic workflows is highly desirable
- Experience with risk, margin and regulatory calculators and reporting capabilities
- Knowledge building high capacity, high volume, low latency technology for margin workflow supporting various business units in Commercial Investment Banking
- Deep knowledge of LLM and API integration, including RESTful API design.
Job Expectations:
- This position is not eligible for Visa sponsorship
- Relocation assistance is not available for this position
- Position offers a hybrid work schedule
Locations:
- 300 S Brevard St., Charlotte, North Carolina 28202
- 194 Wood Ave S, Iselin, New Jersey 08830
Pay Range
Reflected is the base pay range offered for this position. Pay may vary depending on factors including but not limited to demonstrated examples of prior performance, skills, experience, or work location. Employees may also be eligible for incentive opportunities.
$159,000\.00 \- $305,000\.00Benefits
Wells Fargo provides eligible employees with a comprehensive set of benefits, many of which are listed below. Visit Benefits \- Wells Fargo Jobs for an overview of the following benefit plans and programs offered to employees.
- Health benefits
- 401(k) Plan
- Paid time off
- Disability benefits
- Life insurance, critical illness insurance, and accident insurance
- Parental leave
- Critical caregiving leave
- Discounts and savings
- Commuter benefits
- Tuition reimbursement
- Scholarships for dependent children
- Adoption reimbursement
Posting End Date:
27 Jun 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.
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.
Salary Context
This $159K-$305K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $180K across 1937 roles with salary data).
View full AI/ML Engineer salary data →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 3,823 AI roles we're tracking, AI/ML Engineer positions make up 69% 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 $181,170 based on 12,692 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($232K) sits 28% above the category median. Disclosed range: $159K to $305K.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Wells Fargo AI Hiring
Wells Fargo has 23 open AI roles right now. They're hiring across AI/ML Engineer, AI Software Engineer, AI Safety, AI Product Manager. Positions span Charlotte, NC, US, New York, NY, US, Irving, TX, US. Compensation range: $140K - $305K.
Location Context
Across all AI roles, 15% (590 positions) offer remote work, while 3,217 require on-site attendance. Top AI hiring metros: New York (2,643 roles, $211,000 median); San Francisco (2,168 roles, $253,000 median); Los Angeles (1,792 roles, $191,580 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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