I’m an Associate Software Engineer passionate about Artificial Intelligence, Machine Learning, and building intelligent applications. I enjoy working with Python, FastAPI, LLMs, LangGraph, RAG systems, and agentic AI architectures. I’m particularly interested in developing AI-powered solutions, exploring multi-agent systems, knowledge graphs, and retrieval-augmented generation. I also enjoy working on backend development, API integrations, and scalable software systems.
Associate Software Engineer
GreenBridge.AI
- Working on AI-powered knowledge graph and Retrieval-Augmented Generation (RAG) systems for analyzing technical documentation and troubleshooting SME solar inverter faults.
- - Developing backend services and APIs using Python and FastAPI.
- - Working with knowledge graph architectures and AWS Neptune for structured knowledge representation.
- - Exploring entity and relationship extraction using KGGen.
- - Building AI-driven retrieval and reasoning workflows over technical documents.
Software Developer Intern
GreenBridge.AI
- Contributed to backend development of a multi-tenant access governance platform.
- - Developed REST APIs using Python and FastAPI.
- - Worked on multi-tenant Role-Based Access Control (RBAC) and authorization workflows.
- - Implemented authentication and token-based access using AWS Cognito and JWT.
- - Worked with Microsoft Entra ID, OpenID Connect (OIDC), and Microsoft Graph integrations.
- - Contributed to facility claims management and AWS Lambda-based backend workflows.
Built an AI-powered travel planning application using a multi-agent architecture. - Designed agent orchestration using LangGraph. - Implemented supervisor and itinerary planning agents. - Integrated LLM APIs using Groq. - Used PostgreSQL for persistent conversational memory. - Developed structured travel planning workflows with constraints to avoid fabricated travel information.
Knowledge Graph Intelligence Platform
Developing a knowledge graph-based RAG platform to extract, organize, and retrieve structured knowledge from research papers. - Implementing document processing and chunking pipelines using Python. - Building entity and relationship extraction workflows using LLM APIs. - Modeling relationships between research papers, concepts, and scientific findings. - Using Neo4j for graph storage and graph-based retrieval. - Exploring multi-hop reasoning for context-aware question answering.
B.Tech – Electronics and Communication Engineering
National Institute of Technology Silchar (NIT Silchar) · 7.39
Attendee · Oct 24, 2026