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About This Role
About the
Wells Fargo is seeking a Lead Software Engineer – Quantitative Investment Strategy (QIS) \& Agentic AI to join the Equity Derivatives Technology organization within Commercial and Corporate \& Investment Banking. This role is central to the front\-office risk and pricing platform, driving QIS index development, back testing, and risk analytics across equity derivatives.
You will lead the design and development of scalable, low\-latency platforms supporting index construction, historical back testing, and real\-time risk analytics for structured derivatives and trading desks. This is a hands\-on senior role requiring strong expertise in distributed systems, Java, and Python\-based quantitative workflows.
The role also integrates Agentic AI and GenAI capabilities to enhance automation, anomaly detection, and decision support. This is an opportunity to shape next\-generation AI\-enabled risk and trading platforms at scale.
In this role, you will:
- Lead QIS platform engineering initiatives, partnering with front\-office, quantitative research, and trading teams to deliver scalable solutions for index strategy development, back testing, and performance analytics.
- Design and build QIS index lifecycle platforms, including index construction, rebalancing, corporate actions handling, and historical simulation/back testing frameworks across large\-scale market datasets.
- Develop Python\-based quantitative workflows and analytics, enabling rapid prototyping, strategy validation, and automation of research\-to\-production pipelines.
- Architect low\-latency, distributed systems supporting real\-time and intraday risk analytics, index recalculations, and portfolio\-level risk aggregation under strict performance and resiliency requirements.
- Integrate Agentic AI and GenAI capabilities into QIS and risk platforms for automation, anomaly detection, monitoring, and decision support, improving efficiency and insight generation.
- Drive engineering excellence and platform reliability, including data quality controls, model validation support, observability, testing automation, and production readiness, while mentoring teams and ensuring compliance with risk and regulatory standards.
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 hands\-on Python (or equivalent scripting) for time\-series analytics, numerical computing (NumPy/Pandas), and research\-to\-production workflows
- 3\+ years of experience in QIS index development, including index construction, rebalancing, corporate actions, and backtesting frameworks
- 3\+ years designing distributed, low\-latency systems supporting real\-time or intraday analytics and large\-scale market data processing
- 3\+ years of equity derivatives / quantitative finance experience, including pricing models, Greeks, and portfolio risk analytics
- 2\+ years working with AI/ML or GenAI systems, including LLM integration, automation, anomaly detection, or agent\-based workflows
- 2\+ years working with NoSQL databases such as MongoDB or Cassandra, including proficiency in handling massive datasets
- 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, Applied Mathematics, Financial Engineering, or a related field
- Demonstrated experience developing or integrating GenAI solutions, including LLM‑based or agentic systems, applied to risk analytics, decision support, anomaly detection, or surveillance
- 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
- 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 is not eligible for Visa sponsorship
- Relocation assistance is not available for this position
- Position offers a hybrid work schedule
Location:
- 150 E 42nd St. \- New York, NY 10017
- Metro Park, 194 Wood Avenue
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.
$143,000\.00 \- $224,000\.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 $143K-$224K range is below the median 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 Required
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 ($183K) sits 21% below the category median. Disclosed range: $143K to $224K.
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, Chandler, AZ, 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 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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