Integrated M.Sc. Data Science Student @ PSG College of Technology (Graduating 2027)
Building enterprise AI systems using LLMs, Retrieval-Augmented Generation (RAG), Knowledge Graphs, and scalable backend engineering.

Email • LinkedIn • GitHub

I enjoy building AI systems that solve real engineering problems—not just machine learning models.

My interests lie at the intersection of

• Retrieval-Augmented Generation (RAG)
• Knowledge Graphs & GraphRAG
• Enterprise AI
• AI Agents
• Information Retrieval
• Backend Engineering

Previously, I worked as an R&D Embedded Software Intern at ICU Medical, where I automated software validation workflows for IV Pump systems following IEC 62304 medical software practices.

I'm currently researching intelligent enterprise knowledge retrieval using GraphRAG, hybrid retrieval techniques, and knowledge graphs.

• Enterprise Knowledge Management
• Retrieval-Augmented Generation (RAG)
• GraphRAG
• Knowledge Graphs
• Semantic Search
• AI Agents
• Production AI Systems
• LLM Evaluation
• Backend Systems for AI

Building an enterprise knowledge system that transforms organizational knowledge into an AI-ready graph for intelligent retrieval.

Highlights

• GraphRAG architecture
• Knowledge Graph construction
• Hybrid retrieval
• Semantic search
• Metadata-aware retrieval
• Enterprise document understanding

Tech

Python Neo4j LangChain OpenAI FastAPI FAISS Docker

Predicting stock movement by combining financial time-series data with Reddit sentiment.

Highlights

• LSTM forecasting
• Multi-source data fusion
• Financial feature engineering
• Time-series prediction
• NLP-based sentiment analysis

Tech

Python PyTorch LSTM Pandas Scikit-learn

Anonymous social platform with intelligent content ranking.

Highlights

• UCB1 Multi-Armed Bandit ranking
• Semantic similarity using SBERT
• Secure messaging
• Backend-first architecture

Tech

Python SBERT PostgreSQL FastAPI

Exploring how Knowledge Graphs can improve Retrieval-Augmented Generation for enterprise-scale document retrieval.

Current research areas include

• GraphRAG
• Hybrid Search
• Metadata-aware Retrieval
• Knowledge Graph Construction
• Retrieval Evaluation
• Agentic Retrieval Pipelines

R&D Embedded Software Intern

• Automated software validation workflows for IV Pump software.
• Developed Python-based tools to improve testing efficiency.
• Worked within IEC 62304 compliant medical software development processes.
• Collaborated with firmware and software engineering teams.

Worked on machine learning systems involving recommendation models and explainable AI using ensemble learning techniques.

• Python
• SQL
• C++

• Retrieval-Augmented Generation (RAG)
• GraphRAG
• LangChain
• Hugging Face Transformers
• OpenAI API
• Prompt Engineering
• Embeddings
• Semantic Search

• PyTorch
• Scikit-learn
• XGBoost
• SHAP
• LSTM

• PostgreSQL
• Neo4j
• FAISS
• ChromaDB

• FastAPI
• Flask
• REST APIs

• Git
• GitHub
• Docker
• Postman
• Linux

I'm currently exploring

• Agentic AI
• Multi-Agent Systems
• Knowledge Graph Reasoning
• Enterprise Search
• LLM Evaluation
• Advanced Retrieval Systems
• Production AI Infrastructure

I'm interested in collaborating on projects related to

• Enterprise AI
• LLM Applications
• Knowledge Graphs
• GraphRAG
• AI Infrastructure
• Backend Engineering

If you're working on similar problems, feel free to connect.

Thanks for visiting! 🚀