Radnyi Jagtap

As an AI Intern at Owting, I deployed a production-grade conversational AI feature in a mobile app, optimizing chatbot accuracy and response latency through rigorous benchmarking of LLMs. I implemented personalized recommendations and enhanced intent recognition, significantly improving user engagement and satisfaction.

Developed a real-time keyword spotting system for mental health monitoring using Arduino Nano BLE Sense, achieving high validation accuracy through model optimization and data augmentation techniques.

Led the design of a GNN-based Deep Reinforcement Learning framework for robot navigation, reducing collision rates and enhancing trajectory prediction through advanced modeling techniques.

Designed a goal-oriented conversational AI agent capable of resolving multi-turn queries, integrating RAG with FAISS for improved response accuracy. Developed a modular agent loop for self-improvement, enhancing task completion rates significantly.

Pursuing a Master's in Computer Engineering with a focus on advanced topics such as Artificial Intelligence, Deep Reinforcement Learning, Data Mining, and Natural Language Processing.

Completed a Bachelor's in Electronics and Telecommunications Engineering, covering essential subjects like Artificial Neural Networks, Image Processing, and Embedded Machine Learning.