AI/ML and Data Science enthusiast with experience building NLP, Generative AI, and full-stack applications using Python, Flask, React.js, and Scikit-learn. Led the Viswam Telugu LLM project as Tech Lead, contributing 500+ curated Telugu language records and managing a team of 5+ contributors. Skilled in Machine Learning, Data Analysis, Prompt Engineering, REST API development, and AI-powered application design.
Overview
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Certification
Work History
AI & LLM Intern and Tech Lead
Swecha Telangana (Viswam Project)
05.2025 - 07.2025
Contributed to the development of a Telugu Large Language Model (LLM) platform serving regional language users and supporting Telugu NLP research.
Curated and organized 500+ Telugu text records for LLM training and evaluation.
Performed data preprocessing, text cleaning, and normalization on Telugu datasets.
Leveraged Hugging Face and Streamlit to prototype and evaluate multiple AI workflows for Telugu language applications.
Led a team of 5+ student contributors and coordinated project activities.
Participated in 3+ Generative AI awareness programs and regional language technology initiatives.
Participated in Zignasa Hackathon - MLR Institute of Technology.
Solved 100+ Data Structures and Algorithms problems on LeetCode.
Projects
AI-Powered Travel Itinerary Planner, Developed a full-stack AI travel planner using Gemini AI, Flask, React.js, and SQL., Integrated RedBus, IRCTC, Uber, Google Maps, and Weather APIs for travel recommendations., Built 5+ REST APIs for itinerary generation, hotel suggestions, and flight recommendations., Managed 150+ travel itineraries through a structured SQL database system., Python, Flask, React.js, SQL, Gemini AI, REST APIs, Google Maps API, Weather API, Repository, View Project
Mental Health Detection from Social Media Text Using SVM and TF-IDF, Designed an NLP-based classification model for mental health analysis using social media text., Processed and analyzed 5,000+ text records using data preprocessing and feature engineering techniques., Applied TF-IDF vectorization and trained Support Vector Machine (SVM) models for text classification., Achieved 97% classification accuracy on the evaluation dataset., Python, Scikit-learn, NLTK, TF-IDF, SVM, Mental Health Detection