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

Wells Fargo Bank•Iselin, NJ

17h•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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.