Teams at NVIDIA

Design, implement, and maintain infrastructure and CI/CD pipelines to deploy and automate local AI workloads across inference backends. Build model download and repository sync systems, implement data processing and visualizations, measure accuracy and performance, debug existing frameworks, and collaborate with developers to streamline debugging and deployment of AI applications and models.

Lead and grow a team building an on-device AI inference platform for RTX/ DGX GPUs. Drive cross-functional alignment, architect inference runtimes, optimize models and pipelines for low-latency, memory-efficient local deployment, and establish processes for system-level debugging, performance/accuracy trade-offs, and production readiness. Mentor engineers and deliver roadmap-aligned, high-quality releases.

Design, implement, operate and support large-scale GPU cloud PaaS and backend microservices. Drive architecture, test automation, CI/CD, DevSecOps, observability, and collaborate with frontend, UX and stakeholders to deliver highly available, performant distributed systems.