As an experienced lead data scientist with a keen interest in learning and applying deep learning techniques, I have a proven track record of developing and deploying end-to-end machine learning products. In my current role, I have explored various deep generative models such as Variational Autoencoders, Generative Adversarial Networks, and Normalizing Flows, to learn from large datasets in an unsupervised approach for density estimation and anomaly detection. I am proficient in Python, with experience in developing machine learning and data science projects, adhere to PEP 8 style guide and object-oriented programming principles. I have also been exposed to basic MLOps practices throughout the full ML product development lifecycle, including deploying products in a Kubernetes-orchestrated environment and developing Dockerized services. I have worked on AWS Cloud services along with exposure to google cloud and azure. I have used all services in AWS cloud including S3, SQS, SNS, EC2, CLOUDFRONT, LAMBDA etc. I spend time after office hours participating in competitions and book reading which gives me exposure to various state-of-the-art deep learning models in the field of computer vision and NLP, and helps me improve my ability to build better models.