Baseer Ahmad, PhD, is an expert in Intelligent Predictive Maintenance with ML/AI. He has a master's degree in IoT with distinction from the University of Bradford. He has a bachelor's degree in Electronics and Telecom from Sarhad University of Science and Information Technology (SUIT) (Pakistan) in 2015 with distinction, as well as a three-year associate engineering diploma in Electronics from Peshawar Institute of Technology in 2010. His expertise is in embedded electronics systems, IoT, WSN, artificial intelligence, and real-time monitoring using lightweight IoT protocols. Recently, he worked as an Internet-of-Things and Machine Learning/Artificial Intelligence-KTP Associate at the University of the West of Scotland (UWS) on an Innovate-UK (UKRI) funded industrial research project for Hart Lifts Ltd., where he developed a cutting-edge predictive maintenance framework for lifts based on machine learning and artificial intelligence models. In addition, he builds the hardware to an industrial standard for real-time remote monitoring of lift activities and performance of core components according to the UK elevator industry standard, which requires him to make it fire-safe as well as insulate it from all EMI (electromagnetic interference) caused by the lift equipment around it. His designed hardware is capable of working standalone as well as taking information from the lift controller through RS485, CAN, and Modbus protocols. Furthermore, he worked on the Smart Cities and Open Data REuse (SCORE) project, which was funded by the European Commission. He made the air quality, water flow, and rain gauge sensors work in a MeSH network so that they can all connect in challenging situations. He also made the network compatible with LoRaWAN networks so that he could make an IoT solution that could be scaled up.