Lead HPC Cloud Engineer

1. Role Overview

This role leads the development, deployment, and optimization of a global, massive-scale financial pricing compute grid on public cloud infrastructure (AWS/GCP). The team needs this role to operationalize complex quantitative models and efficiently execute trillions of calculations for real-time risk valuation, Monte Carlo simulations, and regulatory reporting across thousands of cores. Its core purpose is to build a highly resilient, cost-optimized valuation engine that enables the firm to price its trading book rapidly and meet critical risk management deadlines.

2. Key Responsibilities

• Architect and manage distributed compute grids on public cloud platforms (AWS/GCP) to execute financial pricing models.
• Design and implement task orchestration layers to distribute millions of tasks across hundreds of thousands of CPU/GPU cores.
• Monitor, tune, and optimize platform performance, cloud cost efficiency, and resource utilization.
• Deploy, version-control, and integrate quantitative pricing models into production in collaboration with quant development teams.
• Build data logistics pipelines to deliver market data, trade data, and model configurations to runtime engines seamlessly.
• Maintain high availability, fault tolerance, and strict recovery time objectives (RTO) for the valuation engine.

3. Core Requirements & Eligibility

• Experience: 10+ years of professional experience building and running applications on massive-scale compute grids.
• Education: Bachelor's or Master's degree in Computer Science, Engineering, or a related technical discipline.
• Domain Exposure: Financial industry experience, specifically with pricing grids, Monte Carlo simulations, VaR (Value at Risk) calculations, or XVA pricing models, is highly preferred.
• Location/Setup: On-site / Hybrid work setup based in Pune or Chennai.

4. Skillset Breakdown

• Primary (Must-Have):

• Cloud Platforms: AWS or GCP

• Orchestration & Containerization: Kubernetes, Docker, AWS Batch, or GCP Batch

• Core Programming: Python

• Infrastructure & Systems: Distributed Systems, High-Performance Computing (HPC), Infrastructure-as-Code (IaC)

• Secondary (Nice-to-Have):

• GPU computing / CUDA

• Financial Quant domain (VaR, XVA, Monte Carlo)

• Serverless compute architectures

• Cloud cost management / FinOps