About

Sahil Shah is a highly skilled software engineer with a passion for algorithmic problem-solving and designing scalable systems. He currently works as a Software Development Engineer at Directi, where he is a part of the Online advertising (Real Time Bidding) team. In this role, he has designed and developed a distributed real-time solution to track bidding revenue and expenditure on several business aggregate levels, saving thousands of dollars per month. He has also improved performance on sections of semi-exploration traffic, doubled revenue rate, and improved profit rate by 50%. Furthermore, Sahil has built a machine learning model with a low latency pipeline for click-through rate prediction on premium customer traffic. Sahil's expertise extends beyond his current role, as he has previously worked as a Software Engineering Intern at PayPal and Directi. At PayPal, he optimized the Rule Recommendation Algorithm for Fraud Detection, implemented the rule recommendation algorithm on a Spark cluster using Spark's ML Lib, and designed a service to measure the effectiveness of Rule Recommender. At Directi, Sahil worked in the Dynamic Margin Management Team, where he evaluated feature encoding schemes to reduce model size and training time, without sacrificing much accuracy. He also built a simulator and dashboard in Flask to train, evaluate, and compare models built on a user-chosen subset of all available features. Sahil holds a Bachelor of Technology degree in Computer Science from the Indian Institute of Technology, Hyderabad. He is proficient in languages such as C, C++, Python, Java, and Go and is interested in reading backend engineering blogs on System Design and Machine Learning. Sahil's tech stack includes Software Engineering, ML, dotNET, Backend, Spark, Java, Python, Flask, Javascript, Kafka, Data Engineering, test, and Docker. With 1.78 years of relevant experience, Sahil is a highly skilled and dedicated software engineer with a passion for solving complex problems and designing scalable systems.