lithik raj

About

Motivated B.Tech student in Artificial Intelligence & Data Science with hands-on experience in machine learning, deep learning, NLP, and Flask-based web applications. Built multiple academic and internship projects spanning predictive modeling, generative AI, and AI-powered recommendation systems.

Experience

• AI & Machine Learning Intern
  Studix Solution
  Dec 2025 – Jan 2026

Education

• B.Tech
  Er. Perumal Manimekalai College of Engineering
  Artificial Intelligence & Data Science · 2024 – 2028
• Class XII
  SRV Boys Matriculation Higher Secondary School
  Biology / Mathematics · 2022 – 2023

Skills

• CSS <3 months
• Deep Learning <3 months
• HTML <3 months
• Python <3 months
• Machine Learning <3 months
• JavaScript <3 months
• NumPy <3 months
• Pandas <3 months
• scikit-learn <3 months
• Flask <3 months
• PyTorch <3 months
• Model Deployment <3 months
• Natural Language Processing <3 months
• Embeddings <3 months
• Generative Adversarial Networks (GANs) <3 months
• Attention Mechanisms <3 months

Tools / apps / platforms

• Git <3 months
• GitHub <3 months
• Hugging Face Transformers <3 months
• Microsoft Office <3 months
• MySQL <3 months

Projects

• NEXUS AI – Unified Employee Service Application

  Team hackathon project at KIT College, Coimbatore.

• Intel AI Assistant
  Flask, OpenRouter API

  Conversational AI assistant with a web front end.

• Student Result Prediction System
  Flask, ML

  Predicts academic performance from historical data.

• Weather Status Prediction
  Flask, ML

  Real-time weather prediction web app.

• Crop Recommendation System
  Flask, ML

  Recommends crops from soil and climate parameters.

• House Price Prediction System
  Flask, ML, HTML/CSS/JavaScript

  Regression model with a glassmorphism UI.

• AI-Powered Resume Screening & Job Recommendation System
  Flask, MySQL, NLP

  ATS-style scoring, skill extraction, and role prediction system.

• COCO 2017 & Oxford-102 Flowers Dataset Exploration
  Python, pandas

  Exploratory analysis of classes, captions, resolutions, and image-text visualization.

• Text Preprocessing & Embeddings
  Hugging Face Transformers

  NLP preprocessing pipeline producing text embeddings for downstream generation tasks.

• Conditional GAN (CGAN) for Shape Generation
  PyTorch

  Baseline conditional GAN generating synthetic shape images from class labels.

• Self-Attention CGAN for Shape Generation
  PyTorch

  Conditional GAN generating 128×128 shapes from category labels, with attention-map visualization.

• Text-to-Image Generation Pipeline
  PyTorch, Hugging Face Transformers

  End-to-end pipeline integrating text preprocessing, embeddings, and image generation.