Market Risk Dev Python, C++

Jersey City, NJ, United States (On-site)

Contract (3 months 18 days)

Published 4 days ago

C / C++

Containerization & Orchestration

Quantitative Computing

AWS/GCP

Python

The Opportunity Can You Build and Command a Cloud Supercomputer

Want to run one of the largest high performance computing grids in the financial industry Are you driven by the challenge of orchestrating trillions of calculations across thousands of cloud cores to price the firms entire trading book in minutes not ours

Do you want to build the massive scale valuation engine that powers Citis next-generation risk platform

Citi is seeking a visionary Cloud HPC Engineer to lead the development and operation of our global pricing grid This is the engine room of our risk system

You will be responsible for deploying scaling and optimizing the computational factory that runs our most complex pricing models at an unprecedented scale on public cloud platforms like AWS and GCP

Your Role and Impact:

As the lead for the Pricing Engine you are the master of massive scale computation

You will take the sophisticated pricing models developed by our top quants and operationalize them on a colossal grid

Your primary mission is to ensure that millions of trades can be revalued against thousands of historical market scenarios with extreme speed efficiency and rock solid stability

Your impact is at the core of our risk valuation capability

You will architect the system that answers the most fundamental question in risk

What is it worth right now under this scenario

The performance and reliability of the platform you build will directly determine the firms ability to manage risk and meet its most critical regulatory obligations

Key Responsibilities:

Architect build and manage a massive scale distributed compute grid on public cloud platforms AWS GCP for running financial pricing models

Design and implement the orchestration layer responsible for distributing millions of pricing tasks efficiently across hundreds of thousands of CPUGPU cores

Deploy manage and version control a diverse library of quantitative pricing models ensuring they run optimally in a distributed environment

Obsessively monitor and optimize the performance cost and resource utilization of the cloud grid driving continuous efficiency improvements

Collaborate with quantitative development teams to seamlessly integrate new and updated pricing models into the production grid

Engineer the data logistics to ensure that the correct market data trade data and model configurations are available for every calculation at runtime

Ensure the pricing engine is highly available resilient and capable of meeting stringent recovery time objectives

What Were Looking For:

10 years of professional experience with a proven track record of designing building and running applications on massive scale compute grids

Expert level hands-on-experience with at least one major public cloud provider AWS or GCP including their batch processing container and serverless offerings

Deep expertise in containerization and orchestration technologies Docker Kubernetes

Strong programming skills in languages common to high performance computing such as C and Python

Prior experience in a similar role within the financial industry eg running large scale Monte Carlo simulations VaR calculations or XVA pricing grids is highly desirable

A degree in Computer Science Engineering or a related technical field

A strong background in distributed systems performance tuning and infrastructure as code principles

Exceptional problem solving skills with an ability to diagnose and resolve complex issues in a high pressure large scale environment

Excellent communication skills and the ability to work effectively with quantitative research trading and risk management teams

The pay range that the employer in good faith reasonably expects to pay for this position is $34.67/hour - $54.18/hour. Our benefits include medical, dental, vision and retirement benefits. Applications will be accepted on an ongoing basis.

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