Lead Engineer, Next Generation Quant Pricing Platform - Vice President

The Opportunity: Can You Build the Industry's Premier Risk Quant Pricing Platform? Are you interested in building the next-generation risk quant pricing platform that runs on large-scale, high-performance compute grids? Are you driven by the challenge of orchestrating trillions of calculations across a hybrid cloud environment to compute risk P&Ls with extreme speed and precision? Do you want to own the core platform that brings sophisticated front-office pricing models to life at an industrial scale?

Citi is seeking a visionary Lead Engineer to architect and build our global Quant Pricing Platform. This is the computational heart of our risk system, where complex quantitative models meet massive-scale infrastructure. You will be responsible for creating a seamless, high-performance ecosystem that spans both in-house and external cloud grids, delivering the computational power our quants need to manage risk effectively.

Your Role and Impact: As the Lead Engineer for the Quant Pricing Platform, you are the master of distributed quantitative computation. You will build the end-to-end system that takes pricing models from front-office quants, deploys them onto a colossal hybrid grid, and orchestrates the massive-scale calculation of risk P&Ls. Your primary mission is to engineer a platform that is incredibly powerful, flexible, and efficient, enabling the firm to run complex scenario-based valuations on demand. Your impact is foundational to the firm's risk management capabilities. You will build the bridge between quantitative research and production-scale execution. The platform you create will directly determine the speed, accuracy, and sophistication of our risk calculations, empowering our business to navigate complex markets with confidence.

Key Responsibilities:

• Architect and build a next-generation, hybrid-cloud platform for running quantitative pricing calculations at massive scale.
• Design a sophisticated job orchestration and scheduling system that handles dynamic prioritization, efficient task packing, and resource optimization across the grid.
• Engineer a robust, event-driven framework for managing the entire job lifecycle, including handling notifications for job completion and managing the flow of result data from the grid.
• Develop a comprehensive error-handling and resiliency strategy to gracefully manage failures in a large-scale distributed environment.
• Pioneer the development of an intelligent monitoring platform using ML to perform anomaly detection, predict job failures, and optimize grid performance.
• Engineer the secure and efficient data logistics required to move terabytes of market and trade data between on-premise systems and public cloud environments.
• Partner closely with front-office quants to onboard, version, and optimize their C++ and Python pricing models for distributed computation.

What We're Looking For:

• Professional experience with a proven track record of designing and building applications for massive-scale, high-performance compute (HPC) grids.
• A degree in a highly technical field such as Computer Science or Quantitative Finance.
• Deep domain knowledge in Capital Markets, financial derivatives, and a strong understanding of financial regulations like FRTB.
• Expert-level experience with job scheduling, resource management, and workload optimization techniques (task packing, prioritization) on large-scale grids.
• Deep expertise in building event-driven architectures and handling asynchronous notifications and replies from distributed systems.
• Proven experience designing and building intelligent monitoring tools, ideally leveraging ML for anomaly detection and performance tuning.
• Expert-level experience managing workloads in a hybrid cloud environment, with deep knowledge of at least one major public cloud (AWS, GCP) and on-premise grid technologies.
• Deep expertise in containerization, orchestration, and scheduling technologies (Docker, Kubernetes).
• Strong programming skills in languages common to high-performance and quantitative computing, such as C++ and Python.
• Proven ability to debug complex pricing and model-related issues by partnering directly with quantitative analysts.
• Prior experience in a similar role within the financial industry, specifically working on quantitative pricing platforms, large-scale risk calculations, or XVA grids.

Education:

• Bachelor’s degree/University degree or equivalent experience
• Master’s degree preferred

Job Family Group: Technology

Job Family: Applications Development

Time Type: Full time