Rahul Juluru

At South Central Climate Adaptation Science Center, I build Python multiprocessing pipelines that cut per-file processing time from four hours to 20 minutes. I validated outputs across more than 375 simulations and built a React dashboard that reduced manual errors by 29%.

As a Graduate Research Assistant at the University of Oklahoma, I designed distributed Python data pipelines for 1.8 TB of research data and increased machine-learning feature-extraction throughput fourfold.

At Neospark, I developed TypeScript and Node.js backend services, improved PostgreSQL query efficiency, and reduced dashboard load time by 25%. I also refactored React and REST API interactions through clear API-contract documentation.

I build AI applications including RAG chatbots, text-to-SQL systems, and receipt categorization pipelines using FastAPI, LangChain, Docker, PostgreSQL, Redis, FAISS, Chroma, and Google Cloud Platform. I focus on rigorous validation, scalable architecture, and maintainable code.