Lead Java Software Engineer – Equity Derivatives Risk, Quant & AI‑Enabled Platforms

Wells Fargo Bank•Iselin, NJ

3d•Hybrid

About The Position

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.

Requirements

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

Nice To Haves

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

Responsibilities

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.