Cloud-Scale Risk Analytics Engineer — Pricing Grid Lead

Capgemini; New York (NY); Hybrid; USD 103,000 - 129,000; Full time

Job summary

Capgemini in the United States (NJ) seeks a Lead for the Pricing Engine to design and operate a massive-scale, distributed compute grid on public clouds (AWS, GCP). You will translate complex pricing models into a scalable platform that can re-value millions of trades across thousands of scenarios.

You will deploy orchestration layers, optimize performance and costs, and collaborate with quantitative teams to integrate models.

Qualifications

• 10+ years of professional experience designing, building, and running large-scale compute grids.
• Expert-level experience with at least one major public cloud (AWS or GCP) including batch processing, containers, and serverless.
• Degree in Computer Science, Engineering, or a related technical field.

Responsibilities

• Architect, build, and manage a massive-scale, distributed compute grid on public cloud platforms (AWS, GCP).
• Design and implement the orchestration layer for distributing pricing tasks across CPU/GPU cores.
• Deploy, manage, and version-control a library of pricing models for a distributed environment.
• Monitor, optimize performance and costs of the cloud grid; drive efficiency improvements.

Skills

Pricing engine

Distributed compute

Cloud platforms (AWS/GCP)

Docker

Kubernetes

C++

Python

Monte Carlo simulations

VaR/XVA pricing

Distributed systems

Infrastructure as code

Problem-solving

Communication

Education

Bachelor's degree in Computer Science or Engineering

Tools

Docker

Kubernetes

Job description

Capgemini in the United States (NJ) seeks a Lead for the Pricing Engine to design and operate a massive-scale, distributed compute grid on public clouds (AWS, GCP). You will translate complex pricing models into a scalable platform that can re-value millions of trades across thousands of scenarios.

You will deploy orchestration layers, optimize performance and costs, and collaborate with quantitative teams to integrate models.