Chandra Kethan
Building RAG architectures and autonomous multi-agent workflows.
Architecting Autonomous Systems
Engineering at the Frontier of RAG & Agents
I am an AI Engineer and author of The AI Engineering Guide, focused on building production-grade Retrieval-Augmented Generation (RAG) architectures and stateful multi-agent workflows with LangGraph and autonomous reasoning loops.
My work spans designing low-latency vector retrieval pipelines, fine-tuning domain-specific rerankers, orchestrating resilient agent debate and tool execution systems, and deploying high-throughput AI backends using FastAPI, Next.js, Docker, and Google Cloud.
I believe in building in public and democratizing systems-level AI engineering—distilling production blueprints, benchmarks, and interview design principles for a community of 10,000+ engineers.
Multi-Agent Orchestration
LangGraph workflows, autonomous tool execution, self-correction, reflection loops, and agent consensus.
Advanced Retrieval & RAG
Hybrid dense/sparse vector search, graph embeddings, domain rerankers, and contextual retrieval pipelines.
Production Systems & Infra
FastAPI services, Next.js full-stack interfaces, Docker, Qdrant/Pinecone, Neon DB, and GCP deployments.