CV

Education

• B.Tech. in Computer Engineering, Vishwakarma Institute of Information Technology, 2026 (expected)
• Diploma in Computer Engineering, Government Polytechnic, 2023

Work experience

• VIIT 2024: Research Assistant
  • Vishwakarma Institute of Information Technology
  • Conducted research on the project “Prediction of Optimal Joint Angles for Prosthetic Knee Design Using Gait Analysis” under the guidance of Dr. Snehal Rathi.
  • Evaluated machine learning models, including RNN, Bidirectional LSTM, and Random Forest, achieving 95% accuracy in predicting joint angles, contributing to advancements in prosthetic technology.
  • Research published in IGI Global’s “Modern Digital Approaches to Care Technologies for Individuals with Disabilities”.
• Aerovania 2023: Flutter Developer Intern
  • Aerovania
  • Developed the Aerovania Flutter app with an intuitive and seamless UI, integrating Riverpod for efficient state management.
  • Optimized loading times by 30% and integrated Firebase, enabling support for over 1K monthly transactions.

Skills

• Programming Languages:
  • C++, Java, Python, C, JavaScript, Dart
• Core Technical Expertise:
  • Artificial Intelligence, Machine Learning, Generative AI, Large Language Models (LLM)
• Frameworks & Tools:
  • Flutter, FastAPI, LangChain, Postman
• Database & Backend:
  • Firebase, MongoDB, ChromaDB, SQL
• DevOps & Cloud:
  • Docker, Azure, GCP (Cloud Services)
• Frontend Development:
  • HTML, CSS, Figma
• Version Control:
  • Git, GitHub
• Other Skills:
  • Data Structures and Algorithms, System Design, Interpersonal Communication

Publications

• “TinyLlama Chatbot for VIIT College”
  Fine-tuned TinyLlama with 4-bit quantization and implemented RAG architecture for enhanced, up-to-date knowledge. Used Baai embeddings model for generating embeddings.
  Published in IJRESM.

• “Prediction of Optimal Joint Angles for Prosthetic Knee Design Using Gait Analysis”
  Conducted under the guidance of Dr. Shehal Rathi. Evaluated RNN, Bidirectional LSTM, and Random Forest models to predict joint angles with 95% accuracy, contributing to advancements in prosthetic design.
  Featured in IGI Global publication “Modern Digital Approaches to Care Technologies for Individuals with Disabilities”.