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🎓 Currently pursuing my master's at NIT Delhi 🧠 Passionate about NLP, LLMs, Generative AI, Deep Learning, and Machine Learning 📊 Quant enthusiast
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a1facts-benchmark
September 7, 2025 – October 19, 2025
a1facts-benchmark — GitHub repository
View ProjectRaptor-based-LLM-Q-A-Chatbot-App-using-hybrid-search-
August 23, 2024 – August 26, 2024
This Project contains a Gradio application for an enhanced Raptor Q&A bot that leverages LLaMA 3.1 With hybrid Retrieval (custom embeddings and BM25) and Pinecone Vector Database. The app allows users to interact with the Model and get responses based on their queries.
View ProjectGraphRAG-with-Hermes-2.5-Pro-LLM-using-neo4j-database
August 20, 2024 – March 28, 2025
This project involves the implementation of a graph-based data storage and retrieval system using Neo4j, a powerful graph database, for the Hermes 2 Pro LLM model. The Neo4j database is deployed as a Docker container to ensure a scalable and isolated environment. The integration enables the Hermes 2 Pro model to efficiently handle complex relations
View ProjectLLaMA-3.1-Q-A-Chatbot-App-utilizing-RAG-
August 14, 2024 – August 14, 2024
"This project leverages LLLaMA 3.1 embeddings for Retrieval Augmented Generation, with the embeddings managed within a ChromaDB instance running as a Docker container. The application interface is built using Gradio
View ProjectFine-Tuning-Model-Pegasus-for-Text-Summarization
August 9, 2024 – August 9, 2024
This repository contains a FastApi application for an enhanced Q&A bot that leverages Pegasus model and FastApi. The app allows users to interact with the Model and get responses based on their queries.
View ProjectNLP-Sentimental-Analyses-using-RoberTa-and-VADER
July 16, 2024 – July 16, 2024
In this project we are Training the VADER (Valence Aware Dictionary and sentiment Reasoner) NLP model and comparing its performance with the pretrained RoBERTa model.
View ProjectNLP_text_summarization
June 11, 2024 – August 9, 2024
Implemented the pipeline in a modular manner, allowing individual components (e.g., data loaders, pre-processing steps, model configurations) to be easily modified or extended.
View ProjectCultural Fit Analysis
The candidate's projects are exclusively personal and heavily focused on AI/ML, particularly NLP and LLMs. While this aligns with a Data Scientist role, the lack of diverse project types, team-based work, or contributions to open-source projects makes it difficult to assess broader cultural fit, adaptability, or experience in different organizational contexts. The experience level is 0, suggesting a very junior profile despite the advanced project topics.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a focus on practical application and integration of AI models. However, without specific assessment data on communication, logical reasoning, or teamwork, it is difficult to assess soft skills and operational fit comprehensively. The project descriptions are clear but lack detail on problem-solving processes or collaboration.