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About

Generative AI Engineer and Computer Science graduate (2026) with experience in designing AI agents, LLM applications, and retrieval-augmented generation (RAG) systems for real-world automation and knowledge-intensive tasks. Skilled in building scalable, production-ready AI solutions involving orchestration, fine-tuning, evaluation, and deployment of large language model applications.

Experience2

  1. Python Developer Intern

    Feb 2025 — Mar 2025

    CODTECH IT SOLUTIONS

    Developed Python applications using 10+ core programming concepts and exception handling to improve reliability. Conducted exploratory data analysis and preprocessing on 2,000+ records with Pandas and NumPy, reducing data inconsistencies by 25% and enabling efficient data analysis.

  2. AWS Cloud Intern

    Jul 2025 — Aug 2025

    MARCELLO TECH

    Gained practical experience on 5+ AWS services including S3, EC2, IAM, CloudWatch, SPICE and QuickSight. Applied cloud-native workflows to deploy scalable AI and analytics solutions, improving business intelligence reporting and facilitating interactive visualization for 1,000+ data records.

Projects3

  • Agentic Corrective RAG System

    Engineered an Agentic Corrective Retrieval Augmented Generation (CRAG) system using Python, LangGraph for orchestration, ChromaDB vector storage, Groq-powered inference, and Tavily web search to dynamically select between local document retrieval and external web sources. Built a Dockerized, Streamlit-based multi-document AI assistant with semantic search and confidence-based retrieval, reducing token usage by 45% and deployed on HuggingFace spaces.

    Open on huggingface.co
    • Python
    • LangGraph
    • ChromaDB
    • Groq
    • Tavily
    • Docker
    • Streamlit
    • HuggingFace

  • VCoder – Fine-Tuned Python Code Generation Model

    Fine-tuned Qwen2.5-Coder-3B instruct model on 15,000+ rows of Python dataset using Transformers, QLoRA, and Unsloth for parameter-efficient adaptation. Benchmarked on 100-task HumanEval subset showing 7-point Pass@1 improvement from 61% to 68%. Deployed a quantized GGUF model using Ollama and published on Hugging Face, attracting 100+ downloads from the open-source community.

    Open on huggingface.co
    • Qwen2.5-Coder-3B
    • Transformers
    • QLoRA
    • Unsloth
    • Ollama
    • GGUF
    • Python
    • HuggingFace

  • Smart Automated Lead Generation & Cold Email AI Agent

    Developed an autonomous AI agent workflow with n8n, Python, JSON, Groq, and GCP capable of processing 100+ leads per execution by integrating web scraping, lead enrichment, and AI-powered emails. Automated website analysis and reduced manual lead qualification and outreach time by over 90%, boosting campaign efficiency.

    • n8n
    • Python
    • JSON
    • Groq
    • GCP

Education1

  • Bachelor of Engineering

    2022 — 2026

    SRM TRP ENGINEERING COLLEGE

    Computer Science and Engineering