Lead Software Engineer – Quantitative Investment Strategy (QIS) & Agentic AI

Wells Fargo

Hybrid

Quick summary

Work type: Hybrid
Location: New York, NY · Iselin, NJ
Salary: $143,000–$224,000 / yr
Posted: 2 days ago
Closes: Jun 28, 2026

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $206k

This role $184k

$132k most similar roles pay here $243k

This role pays less than 74% of similar roles. Most pay $181,650–$230,400 — the shaded band above. At the midpoint, this role pays about $184k versus about $206k for comparable roles.

Based on 240 similar postings.

Employer

About Wells Fargo

Wells Fargo & Company is one of the largest banks in the United States, providing banking, investment, mortgage, and consumer and commercial finance products and services nationwide. Industry: Banking & Financial Services

Wells Fargo currently has 91 open roles on FindRole.

Listed pay typically runs $151,000–$239,000 across 54 roles with salary data.

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At a glance

TL;DR · Lead Software Engineer – Quantitative Investment Strategy (QIS) & Agentic AI

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Wells Fargo is hiring a Lead Software Engineer for its Equity Derivatives Technology team, focusing on Quantitative Investment Strategy (QIS) & Agentic AI. This senior role involves leading the design and development of scalable platforms for index construction, back testing, and real-time risk analytics in equity derivatives. The engineer will work closely with front-office teams to build Python-based quantitative workflows and integrate advanced AI capabilities like LLMs and agentic systems to enhance automation and decision support. Key responsibilities include architecting low-latency distributed systems, ensuring data quality controls, and mentoring teams while adhering to regulatory standards. Candidates should have extensive experience in Java, Python, NoSQL databases, and equity derivatives, with a focus on QIS index development and AI integration.

Skills

Python Java NumPy Pandas Docker Kubernetes NoSQL MongoDB Cassandra AI/ML GenAI LLM GitHub Copilot IDEs CI/CD Observability Testing Automation Equity Derivatives Quantitative Finance Greeks VaR CCAR FRTB DV01 PnL Attribution

What you'll do

Lead the design and development of scalable platforms for QIS index strategy, back testing, and performance analytics.
Build Python-based quantitative workflows enabling rapid prototyping and automation of research-to-production pipelines.
Architect low-latency distributed systems supporting real-time risk analytics and portfolio-level risk aggregation.
Integrate Agentic AI and GenAI capabilities to enhance automation, anomaly detection, and decision support in QIS platforms.
Drive engineering excellence by ensuring data quality controls, model validation, observability, testing automation, and compliance.

What we're looking for

5+ years of hands-on Python experience for time-series analytics, numerical computing, and research-to-production workflows
3+ years of experience in QIS index development including construction, rebalancing, corporate actions handling, and backtesting frameworks
3+ years designing distributed, low-latency systems supporting real-time or intraday analytics with large-scale market data processing
2+ years working with AI/ML or GenAI systems for automation, anomaly detection, monitoring, and decision support
Strong equity derivatives / quantitative finance experience including pricing models, Greeks, and portfolio risk analytics

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