Lead Engineer, Next Generation Quant Pricing Platform - Vice President

Description

The company is building a new quant pricing platform that serves as the computational core of its risk system.

The platform runs quantitative pricing models at large scale across a hybrid grid, spanning in-house systems and external cloud grids, to compute risk P&Ls quickly and accurately.

The role is a Lead Engineer position at Vice President level, focused on architecting and building the company's global platform.

Responsibilities

• Lead the design and build of a new hybrid-cloud platform that runs quantitative pricing calculations at very large scale.
• Build the end-to-end path that takes pricing models from front-office quants, deploys them to the hybrid grid, and runs large scenario-based risk P&L calculations on demand.
• Design a job orchestration and scheduling system that manages dynamic prioritization, efficient task packing and resource use across the grid.
• Build an event-driven framework to manage the full job lifecycle, including completion notifications and the flow of result data back from the grid.
• Create a comprehensive error-handling and resiliency strategy for failures in a large distributed environment.
• Lead the development of an ML-based monitoring platform for anomaly detection, job failure prediction and grid performance optimization.
• Build secure, efficient data pipelines that move terabytes of market and trade data between on-premise systems and public cloud.
• Work closely with front-office quants to onboard, version and optimize their C++ and Python pricing models for distributed computation.

Requirements

• Professional experience designing and building applications for massive-scale, high-performance compute (HPC) grids, with a proven track record.
• A degree in a technical field such as Computer Science or Quantitative Finance.
• Deep knowledge of capital markets and financial derivatives, with a solid understanding of regulations such as FRTB.
• Expert experience with job scheduling, resource management and workload optimization on large grids, including task packing and prioritization.
• Deep expertise in event-driven architectures and in handling asynchronous notifications and replies from distributed systems.
• Proven experience building intelligent monitoring tools, ideally using ML for anomaly detection and performance tuning.
• Expert experience managing workloads in a hybrid cloud, with deep knowledge of at least one major public cloud (AWS or GCP) and on-premise grid technologies.
• Deep expertise in containers and orchestration, including Docker and Kubernetes.
• Strong programming skills in C++ and Python, or similar languages used in high-performance and quantitative computing.
• Proven ability to debug complex pricing and model issues while working directly with quantitative analysts.
• Prior experience in a similar role in financial services, working on quantitative pricing platforms, large-scale risk calculations or XVA grids.
• A bachelor's degree or equivalent experience is required; a master's degree is preferred.

Conditions

Full-time position.

The job belongs to the Technology job family, within Applications Development.

Equal-opportunity policy information is available from the company's EEO policy statement and the Know Your Rights poster.