Lead HPC Cloud Engineer

Job description

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