Jayanth varma Dantuluri

• Designed and built end-to-end GenAI prototypes, and successfully validated them through functional user interfaces. • Implemented RAG architectures for the AI meeting copilot. • Built proof-of-concepts (PoCs) for GenAI-powered knowledge copilots and digital assistants, evaluating multiple model architectures and interaction patterns. • Developed scalable AI backends using Python, LangChain, FastAPI, Docker, and WebSockets, enabling real-time interaction and low-latency communication.

Master Thesis • Applying Sensor fusion (Radar + 3D time of flight) and AI for the detection and tracking of humans in real-time. • Built a novel sensor fusion approach based on Time-of-flight and radar sensors. • Performed object detection on images from the Time-of-flight sensors, and applied image processing and signal processing algorithms on the radar data. • Framework: Pytorch, Tensorflow

Firstly, I have worked in the company as an Intern for 2 months. During this period, I worked on Databases (MySQL, Postgresql) and code debugging (Python). Later, they offered full time position. During my stay in the company. I have worked as back-end developer. My role is to write back-end code for the applications using python and database design (dynamodb). I have acquired good knowledge on python programming and database technologies.

• Data Mining: Supervised Learning methods | Unsupervised Learning methods | Association rule Learning • Machine Learning: Linear Regression | Naïve Bayes | Decision Trees | Random Forests | SVM | Clustering | Neural Networks | Reinforcement Learning | Semi-Supervised Learning • Deep Learning: Convolution Neural Networks | Recurrent Neural Networks | LSTM | GRU | Autoencoders | Transformer | BERT | GPT • Data Science with R | Visual Analytics | Information Retrieval | Database Management