Likhith Bavisetti — RAG Systems Engineer & LLM Developer
Likhith Bavisetti is a software developer and AI engineer specializing in
Retrieval-Augmented Generation (RAG), multi-agent AI systems, and scalable backend architectures.
With hands-on experience building end-to-end LLM applications using Python, FastAPI, LangChain,
pgvector, AWS Bedrock, and GCP Vertex AI.
About Likhith Bavisetti
I'm Likhith Bavisetti — a graduate software developer specializing in AI-powered systems,
Retrieval-Augmented Generation (RAG), and agent-based architectures.
Skilled in designing scalable data pipelines, integrating vector search and embeddings,
and deploying production-ready solutions across AWS Bedrock and GCP Vertex AI.
I've shipped real-world systems including voice-based AI assistants,
domain-specific RAG pipelines, and multi-agent workflows.
Projects by Likhith Bavisetti
- MNEME — Multi-agent AI system with a living Neo4j knowledge graph. Seven specialized agents collaborate — each query teaches the system something new.
- MediRag — Advanced RAG system for healthcare cost estimation using PostgreSQL + pgvector, LangChain, and GPT-5.
- Hospital Call Assistant — AI-powered phone assistant for hospitals built with FastAPI, Twilio, Dialogflow CX, and Firestore.
- Community Connect RAG — AI chatbot using UMSL newsletters as its knowledge base, powered by a fully local RAG pipeline.
Skills
Python, JavaScript, React, Node.js, Next.js, FastAPI, LangChain, CrewAI, pgvector, ChromaDB, Neo4j, AWS Bedrock, GCP Vertex AI, Docker, Kubernetes, Terraform, CI/CD, Git.
Contact Likhith Bavisetti
GitHub |
LinkedIn