Bhavesh Sidhwani

Led the development of an AI-powered slot-filling recruitment chatbot and a multi-agent LangChain system for query orchestration. Implemented context management to preserve conversation history and job metadata across sessions. Integrated MCP for job posting retrieval and automated creation with a human-in-the-loop workflow, resulting in faster responses and improved posting accuracy. Tech stack included Python, LangChain, TypeScript, React, and Node.js.

Built a RAG pipeline powering seven AI agents for infrastructure security, management and compliance. Developed an infra-scanner Go service that converts observability data into a knowledge graph persisted on GraphDB, and integrated a vector DB to enable subgraph retrieval, reducing token usage by 75%. Designed a continuous evaluation pipeline (RAGAS) to monitor agent performance and prevent model drift. Engineered a dual-LLM critique feedback agent to boost relevancy evaluation.

Built AI-native cloud reliability platform with Next.js frontend, Go backend, and Python AI microservices on GCP. Developed multi-cloud infra scanner in Go to construct digital twin graphs with 50K+ nodes persisted on FalkorDB. Architected modular agent framework, reducing new agent development time from 2 weeks to 4 days. Implemented REST APIs for infra operations and WebSocket APIs for agent interactions with sub-200ms latency. Designed MongoDB session management with hierarchical summarization, enabling 35% longer conversations. Reduced RAG token usage by 75% through MilvusDB vector store with targeted sub-graph retrieval.