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

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