Ritesh is an MS Computer Engineering student at NYU Tandon with ~4 years of industry experience spanning ML inference optimization, data engineering, and production AI systems. He has built TensorRT pipelines that cut latency by 35%, deployed vLLM at scale on T4/A100 GPUs, and shipped distributed training jobs with DDP/FSDP. Currently, he's building an autonomous vehicle anomaly detection system using NVIDIA's Cosmos-Reason2-2B and a multi-view camera classification engine. A hackathon regular, he won the Qualcomm Edge AI and Visa Challenge tracks at HackNYU and most recently built ClawFin at the AI Tinkerers NYC ClawHack. He's interested in agentic AI, GPU systems, and anything that ships.

Agentic AI infrastructure, multi-agent orchestration, and runtime enforcement for autonomous agent systems. Interested in spec-driven development for AI agents, scaling GPU inference at the edge, and swarm detection in production environments. Looking to go deeper on Rust for high-performance AI platforms, ontology-backed agent architectures, and zero-trust security patterns for multi-agent workflows. Also exploring serverless and containerized approaches to deploying agentic AI SDKs.

Building an autonomous vehicle semantic anomaly detection system using NVIDIA Cosmos-Reason2-2B with a multi-view camera pipeline and 8-layer classification engine. Developing ClawFin, a multi-agent deal negotiation platform using OpenClaw, XMTP, and OpenRouter. Collaborating on LLM-based homework feedback analysis research at NYU Tandon. Experimenting with zero-cost autonomous pipelines using LangGraph, MCP, and Google Cloud Run with GPU inference for agentic AI workflows.