With over 14 years of industry experience, I am a seasoned machine learning engineer and leader who has worked on various web-scale problems at Meta, Twitter, Pinterest, and Uber. My expertise lies in building and deploying scalable and robust ML solutions for recommender systems, large language models, Embeddings (Representation Learning), fast vector search, and generative AI. Currently, I am a Senior Staff Machine Learning Engineer at Uber, where I lead the ML team for growth and marketing. I am responsible for developing and optimizing content recommendations, email and push notifications, and creative optimization for Uber and UberEats. Additionally, I am an active investor and advisor for several early-stage startups in the AI/ML domain, such as ChromaDB, Observe.ai, and many more. I am also a co-inventor of a patented algorithm for dynamic updates in hierarchical navigable small world graphs(HNSW), which is open-sourced and widely used in industry for fast vector search. Furthermore, I am an author of a KDD paper on embeddings and approximate nearest neighbor search based candidate generation for recommender systems at Twitter. My mission is to leverage my ML skills and knowledge to create positive impact and value for users, businesses, and society.