Driving and delivering NLP projects across several Intuit products. Dabbled around in independent research while at Intuit, which led to publishing 3 papers and 7 issued patents (10 applied). My research is primarily focused on adversarial learning applications to improve NLP models. Throughout my experience in university projects and in industry, I have had the chance to work on a wide range of classical machine learning and deep learning models and techniques.In NLP, I have experience with training, evaluating, and deploying (in several cases) LLMs including multimodal LLMs, early transformer based models such as Bert and its derivatives, neural network based architectures like RNNs/LSTMs, attention mechanisms, word embeddings, and classical featurization and modeling techniques such as alignment matrices. I have worked on a large variety of NLP applications - GenAI applications using open source LLMs and GPT models, question answering, information retrieval, information extraction, named entity recognition, text classification, and topic modeling.I have explored computer vision mainly through projects at university, and few hobby projects afterwards. I have had the chance to work on multimodal LLMs, GANs, convolutional networks, and classical models such as Haar classifier. Few of my projects are on tasks like image colorization, image classification, information extraction.