About

Harshit Jaitly is a results-focused technology professional with a diverse background in software development lifecycle and data science. He is well-versed in promoting objective-oriented approaches to real-time software development and is an analytical and detail-oriented technology geek. Currently, Harshit works as a Software Engineer with Zomato, where he has worked on migrating existing features from a monolithic-based architecture to a new aggregated microservices-based architecture for operations. He has also implemented functionalities for an upcoming system called "Walker" in the Logistics Domain and its inclusion in the existing microservices architecture and gateway aggregator. Harshit has fixed potentially exploitable ends in Authentication Service and performed Database Optimization resulting in reduced space usage by 88% for storage of Authentication Details. He has also updated old code bases to modern development standards, improving functionality. Harshit's skills include OOP, Databases, Microservices, Go (Programming Language), Docker, MVC, gRPC, MySQL, Ruby on Rails, and Kibana. Prior to Zomato, Harshit worked as a DeFi Researcher with Stader Labs, where he investigated liquid staking solutions to devise appropriate offering strategies for Stader's flagship liquid token FTMx for the Fantom Ecosystem. He also evaluated the existing token-flow of competitors and estimated potentially-profitable partnerships with several Decentralized Exchanges and Liquidity Pools. Harshit played a key role in the launch of Stader's Liquid Token on Fantom Chain resulting in $120 Million worth of $FTM token being staked on Stader platform over a period of 2 months. His skills include Crypto, Web3, Market Analysis, and Marketing. Harshit has also worked as an Intern with Samsung, where he was part of The Data Intelligence Lab at the Services PF and Big Data Group. He researched techniques for Low Latency and Efficient Content-Based Image Retrieval in Large-Scale Image Databases using Feature Extraction. He designed a Feature Extracting Framework based on Deep-Learning using Convolutional Neural Networks and implemented Rapid Search Mechanisms for Quick Retrieval in a highly dense Feature Space. Harshit built an end-to-end recommendation system by scripting and abstracting the complete pipeline as a RESTful API that can be integrated with the flagship app. His domain expertise includes Deep Learning, Digital Image Processing, Computer Vision, and Database Management. His tools/technologies include TensorFlow, Python, Keras, PyTorch, Anaconda, Streamlit, Flask, Docker,