Vikas V

About

B.Tech graduate in Artificial Intelligence and Data Science with hands-on experience in Python full stack development, AI/ML model deployment, and database management. Skilled in building end-to-end web applications and backend services using Python, Django, and related web technologies.

Experience

• Generative AI Intern
  Zepher Technologies · Bangalore
  2024 – 2024

  Developed and deployed Generative AI prototypes using LLM frameworks for intelligent text generation and automated response pipelines. Engineered Python-based backend services and integrated AI models into web applications for real-time inference and user interaction. Built and optimized data pipelines in collaboration with cross-functional teams, improving AI model performance and system reliability. Gained hands-on experience with API integration, backend service design, and deploying ML models in a production-like environment.

Education

• B.Tech
  Sengunthar Engineering College
  Artificial Intelligence and Data Science · 2022 – 2026

Skills

• SQL
• Bootstrap
• Python
• Data Science
• CSS3
• JavaScript
• Django
• Matplotlib
• NumPy
• Pandas
• PyTorch
• TensorFlow
• Seaborn
• Django Rest Framework
• API Integration Understanding
• Version Control
• Natural Language Processing
• HTML5
• Oracle SQL
• RAG
• Agile
• LLMs
• Oop
• MVC
• Generative AI (ChatGPT, Copilot, Gemini)

Projects

• Real-Time Fake News Detection System
  Python, TensorFlow, LSTM, NLP, Flask

  Designed a real-time streaming fake news detection system using LSTM-based NLP models for low-latency text classification. Preprocessed and vectorized large-scale news datasets using tokenization, embeddings, and padding techniques. Exposed the trained model through a REST API using Flask, enabling integration with frontend interfaces.

• Commodity Price Prediction – LSTM + LLM with RAG
  Python, TensorFlow, PyTorch, LangChain, RAG

  Architected a hybrid deep learning pipeline combining LSTM time-series networks with LLM and Retrieval-Augmented Generation (RAG) for commodity price prediction. Integrated external knowledge sources through RAG to enrich prediction context and improve model generalizability. Built interactive visualizations using Matplotlib and Seaborn to present prediction outputs and insights.

• Python Full Stack To-Do Web App
  Python, Django, SQLite, HTML, CSS, Bootstrap, JavaScript, Git

  Developed a full-featured task management web application using Django MVT architecture with CRUD operations, user authentication, and session management. Designed a responsive frontend using HTML5, CSS3, Bootstrap, and JavaScript for dynamic user interactions. Implemented Django ORM for SQLite database operations and developed RESTful endpoints consumed by the frontend JavaScript layer. Managed the project lifecycle on GitHub using feature branches, clean commits, and pull-request practices.