🚀 AI Researcher & Deep Learning Engineer | NLP, LLMs, RLHF, Kaggle GM | Building open-source tools & publishing cutting-edge research.
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IIIT Hyderabad
Deep learning Engineer
June 13, 2026 – Present
codetoprompt
June 11, 2025 – Present
Transform any codebase, web page, or document into an optimized LLM prompt. CodeToPrompt intelligently compresses code and filters content to overcome context window limits.
View ProjectRAG-with-Agents-llama3
May 15, 2024 – June 27, 2024
AI-Powered PDF Query: LangChain ReAct agents with Qdrant and Groq's llama3 for intelligent document retrieval.
View ProjectRayQdrantFastEmbed
March 9, 2024 – March 12, 2024
Generating embedding for 1000s of PDF Documents, in Qdrant using FastEmbed with distributed Computing in Ray
View ProjectFalcon-Local-AI-Model
June 13, 2023 – June 14, 2023
Explore this GitHub repository housing 3 versions of Falcon code for text generation. Each version leverages distinct libraries and models, enabling diverse approaches to generating textual responses. Discover the possibilities within!
View ProjectDetectron-Layout-Parser
June 12, 2023 – June 12, 2023
This code performs PDF layout analysis and optical character recognition (OCR) using the layoutparser library and Tesseract OCR Engine. It detects the layout of a PDF document and extracts text from specific regions. The code is divided into several sections, each serving a specific purpose.
View Projectchat-with-multiple-pdf
May 28, 2023 – June 12, 2023
This project allows you to engage in interactive conversations with your PDF documents using LangChain, ChromaDB, and OpenAI's API. With this powerful combination, you can extract valuable insights and information from your PDFs through dynamic chat-based interactions.
View ProjectNLP-ProjectArchive
May 26, 2023 – June 12, 2023
Contains Models for : Question Answering, Future Word Prediction, POS Tagging, Word Embedding, Analyising Twitter Scaped Data, Semantic Similarity
View ProjectQuestion-Answering-Model-BERT-BIDAF-BILSTM
May 26, 2023 – June 12, 2023
Fine-tunned and Ensembled BERT + BIDAF + BiLSTM + Distil Bert each trained for the task of Question-Answering for the SQUAD v2.0 Dataset
View ProjectCultural Fit Analysis
The candidate's project portfolio shows a strong interest and self-motivation in the Deep Learning domain, which aligns well with a research-oriented or innovative culture. The diversity of projects, from prompt optimization to PDF layout analysis and RAG pipelines, suggests adaptability and a broad technical curiosity. However, the lack of team-based projects or professional experience outside of a current 'Deep learning Engineer' role (with no start date) makes it difficult to fully assess cultural fit in a collaborative corporate environment.
Soft Skills & Operational Fit
Insufficient data to assess soft skills and operational fit. The candidate's project descriptions indicate a proactive and self-driven approach to learning and applying advanced AI concepts.