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About this role:
Wells Fargo is seeking a Lead Java Software Engineer to join the Equity Derivatives Technology organization within Commercial and Corporate \& Investment Banking Technology. This role sits at the heart of the front‑office risk and pricing platform, supporting mission‑critical trading and risk workflows across the equity derivatives business.
You will play a key role in modernizing and scaling Wells Fargo’s strategic Risk \& Pricing platform, enabling real‑time and intraday risk analytics for a broad set of derivative desks, including Index and Single‑Stock Flow, Delta‑1, Equity Finance \& Futures, Convertibles, Corporates, and Structured OTC / Notes. This is a hands‑on senior engineering role designed for a highly quantitative risk developer with experience building large‑scale, distributed systems capable of processing substantial data volumes under strict latency, throughput, and resiliency requirements. You will design and build cloud‑compliant, low‑latency, high‑throughput platforms, working closely with front‑office stakeholders, quantitative teams, and production partners.
As the platform continues to evolve, this role will incorporate applied AI and GenAI‑enabled capabilities to enhance risk analytics, intraday monitoring, automation, anomaly detection, and decision support. If you thrive on solving complex problems at scale and want to influence the next generation of risk and trading technology using modern engineering and AI‑assisted practices, this role offers both depth and impact.
In this role, you will:
- Lead and influence strategic technology initiatives across the Equity Derivatives Risk domain, partnering with front‑office, quantitative, and platform stakeholders to deliver high‑impact outcomes.
- Architect, design, and build scalable, low‑latency applications and microservices supporting real‑time pricing, risk, analytics, and simulations for complex derivative products.
- Own and evolve core risk and pricing frameworks, ensuring performance, resiliency, scalability, and extensibility across multiple trading desks and products.
- Design and deliver large‑scale, distributed systems capable of handling high‑volume, high‑velocity datasets under strict latency and availability requirements.
- Champion engineering excellence, establishing and driving best practices around system design, performance optimization, clean code, testing, observability, and operational resilience.
- Drive automation in testing, documentation, and deployment to improve reliability, scalability, and speed to market.
- Collaborate closely with production support and platform engineering teams to ensure smooth day‑to‑day operations, rapid incident resolution, and continuous improvement.
- Mentor and develop engineers, fostering a culture of technical excellence, ownership, quantitative rigor, and continuous learning, while ensuring compliance with enterprise risk management and regulatory requirements.
Required Qualifications:
- 5\+ years of Specialty Software Engineering experience, or equivalent demonstrated through one or a combination of the following: work experience, training, military experience, education
- 5\+ years of hands‑on Core Java development, with deep expertise in memory management, garbage collection tuning, multithreading, concurrency models, native I/O, and JNI
- 3\+ years designing and building distributed systems, delivering low‑latency, high‑throughput, highly available, and fault‑tolerant architectures using event‑driven and microservices patterns
- 2\+ years of experience with in‑memory data grids (e.g., Oracle Coherence, Apache Ignite, GemFire)
- 2\+ years working with NoSQL databases such as MongoDB or Cassandra, including schema design and query optimization
- 1\+ years of solid experience in modern AI development tooling, including AI‑assisted coding tools (e.g., GitHub Copilot) and contemporary IDEs
Desired Qualifications:
- Bachelor’s degree or higher in Computer Science, Engineering, or a related field
- Strong experience in capital markets workflows, including trade capture, lifecycle management, pricing, and risk analytics, with hands‑on exposure to equity derivatives products (options, futures, swaps, exotics, synthetics, convertibles, prime brokerage)
- Deep understanding of derivative pricing, valuation, and risk methodologies, including Greeks, VaR, CCAR, FRTB, DV01, and PnL attribution
- Proven ability to design, build, or integrate scalable front‑office risk and analytics components with trading platforms
- Demonstrated experience developing or integrating GenAI solutions, including LLM‑based or agentic systems, applied to risk analytics, decision support, anomaly detection, or surveillance
- Strong software engineering fundamentals: algorithms, data structures, performance optimization, clean code, and scalable system design
- Effective communicator able to work directly with front‑office and business stakeholders in fast‑paced, high‑pressure environments
Job Expectations:
- This position offers a hybrid work schedule
- Relocation assistance is not available
- Visa sponsorship is not available
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.
$191,000\.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 $191K-$305K range is above the 75th percentile for AI Software Engineer roles in our dataset (median: $190K across 219 roles with salary data).
Role Details
About This Role
AI Software Engineers build the applications and systems that AI models run inside. They own the API layers, data pipelines, frontend integrations, and infrastructure that turn a model into a product users interact with. Every AI company needs engineers who can build the software around the AI.
The challenge is building reliable systems around inherently unreliable components. Models are probabilistic. They'll give different answers to the same question. They hallucinate. They're slow. They're expensive. Your job is to build an application layer that handles all of this gracefully while delivering a product that users trust and enjoy.
Across the 3,823 AI roles we're tracking, AI Software Engineer positions make up 7% of the market. At Wells Fargo, this role fits into their broader AI and engineering organization.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
What the Work Looks Like
A typical week includes: building API endpoints that serve model inference with caching and fallback logic, designing the data pipeline that feeds context to a RAG system, implementing streaming responses in the frontend, debugging a race condition in the async inference pipeline, and optimizing database queries for the vector search layer. It's full-stack engineering with AI at the center.
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
Skills in Demand for This Role
Full-stack engineering skills with AI integration experience. Python and TypeScript are the most common requirements. You'll need to understand API design, database architecture, and how to build reliable systems around probabilistic outputs. Experience with streaming, async processing, and caching patterns is increasingly important as real-time AI applications proliferate.
Knowledge of vector databases, embedding APIs, and LLM integration patterns (function calling, structured outputs, retry logic) differentiates AI software engineers from general software engineers. Understanding cost optimization (caching strategies, model routing, batched inference) is valuable since inference costs can dominate application economics.
Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
Compensation Benchmarks
AI Software Engineer roles pay a median of $232,000 based on 797 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($248K) sits 7% above the category median. Disclosed range: $191K 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
AI roles in New York pay a median of $211,000 across 2,643 tracked positions. That's 5% above the national median.
Career Path
Common paths into AI Software Engineer roles include Software Engineer, Full-Stack Developer, Backend Engineer.
From here, career progression typically leads toward Staff Engineer, AI Architect, Engineering Manager.
If you're a software engineer, you're already 80% there. Learn the AI integration patterns: RAG, streaming inference, function calling, structured outputs. Build a project that demonstrates you can wrap an AI model in a production-quality application with proper error handling, caching, and user experience. That's the portfolio piece that gets you hired.
What to Expect in Interviews
Technical screens look like standard software engineering interviews with an AI twist. Expect system design questions about building reliable applications around probabilistic models: handling streaming responses, implementing retry logic for API failures, and designing caching strategies for LLM outputs. Coding rounds test standard algorithms plus practical integration patterns like async processing and rate limiting.
When evaluating opportunities: Strong postings describe the product you'll be building, the AI integration patterns you'll work with, and the scale requirements. Look for companies that have existing AI features and need engineers to improve and expand them, not companies that are 'planning to add AI' someday.
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).
AI Software Engineer roles are among the most numerous in the AI job market. Every company deploying AI needs software engineers who understand AI integration patterns. The demand is broad, spanning startups to enterprises, across every industry adopting AI capabilities.
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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