Quantitative Engineer

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• Architect, build, and manage a large-scale distributed compute grid on AWS or GCP for financial pricing models. \n

• Design and implement orchestration for distributing millions of pricing tasks across extensive CPU and GPU fleets. \n

• Deploy, manage, and version a library of quantitative pricing models for optimal performance in distributed environments. \n

• Monitor and optimize performance, cost, and resource utilization to drive continuous efficiency gains. \n

• Collaborate with quantitative development teams to integrate new and updated models into production workflows. \n

• Engineer robust data logistics to deliver market data, trade data, and configurations at runtime. \n

• Ensure high availability, resiliency, and adherence to recovery time objectives for the pricing engine. \n

We are seeking a Qualitative Engineer, Massive-Scale Pricing Engine to join our dynamic team. The ideal candidate will have strong experience in Python, distributed systems, cloud/grid computing, and market risk pricing platforms and a proven ability to design, scale, and operate high-performance pricing grids that deliver fast and reliable valuation at enterprise scale., * 10+ years designing, building, and operating applications on large-scale compute grids. \n

• Expert hands-on experience with AWS or GCP, including batch processing, containers, and serverless services. \n

• Deep expertise with Docker and Kubernetes. \n

• Strong programming skills in Python and C++ for high-performance computing. \n

• Background in distributed systems, performance tuning, and infrastructure as code. \n

• Experience with large-scale Monte Carlo simulations, VaR, or XVA pricing grids in financial services is desirable. \n

• Excellent problem-solving skills and the ability to operate in high-pressure environments. \n

• Clear communication skills with stakeholders in quantitative research, trading, and risk. \n, * Experience with market risk platforms, grid schedulers, and pricing engines. \n

• Familiarity with cost optimization and observability in large cloud estates. \n