Muhammad Umair
About Muhammad Umair
Muhammad Umair is a Machine Learning Engineer currently working at DBtune in Lund, Sweden, where he contributes to the development of SaaS products and optimizes database management systems. He has a background in economics and mathematics, with experience in research and teaching roles at various institutions in Germany and Pakistan.
Current Role at DBtune
Currently, Muhammad Umair serves as a Machine Learning Engineer at DBtune, a position he has held since 2020. In this role, he engages in a diverse range of tasks that encompass backend, frontend, and Site Reliability Engineering (SRE). His contributions are integral to the development of both frontend and backend systems, utilizing technologies such as Python, Node.js, and AWS. As the first employee at DBtune, he played a significant role in the foundational development of the company's SaaS product.
Previous Experience in Machine Learning and Research
Before joining DBtune, Muhammad Umair gained valuable experience in the field of machine learning and research. He worked as a Research Assistant at Heidelberg University from 2018 to 2019, where he contributed to various projects. He also served as an Associate Researcher at Freie Universität Berlin for five months in 2020. His early career included a six-month position at SAP as a Masters Student in Walldorf, Germany, where he further honed his skills in machine learning applications.
Education and Academic Background
Muhammad Umair holds a Bachelor of Science (BS) in Economics and Mathematics from the Institute of Business Administration, which he completed from 2013 to 2017. He furthered his education at Heidelberg University, where he earned a Master of Science (MSc) in Scientific Computing with a focus on Machine Learning from 2017 to 2020. During his undergraduate studies, he also served as a Teaching Assistant at the Institute of Business Administration in Pakistan for one year.
Technical Skills and Contributions
In his current role, Muhammad Umair applies machine learning techniques to optimize the performance of various database management systems, including PostgreSQL, FoundationDB, RocksDB, MySQL, Amazon RDS, and Azure flexible server. He designs and implements pipelines for the experimentation phase of proof of concept on AWS, automating processes using shell scripting, Terraform, and Python. His technical expertise spans multiple programming languages and cloud services, contributing to the efficiency and effectiveness of DBtune's offerings.