Leading Core RAG & AI Team • Scaling AI & Backend Systems
Built production-ready RAG system without vendor lock-in, handling real-world workloads
Designed multi-agent workflows with state management, improving throughput by 30%
Developed advanced agentic RAG system with multiple memory layers, increasing contextual accuracy by 45%
Implemented time-aware RAG system with temporal context understanding for recency-based retrieval
Developed web search integration with deep answer feature combining multiple sources and fact-checking
Implemented full-fledged citation system with proper referencing of files, web sources, and memory
Reduced hallucinations by 98% using hybrid search and source-grounding
Built internal evaluation benchmark, cutting evaluation time by 99%
