Rishab Kulkarni
About Rishab Kulkarni
Rishab Kulkarni is a Senior Engineer at Bosch Global Software Technologies, specializing in automotive electrification and artificial intelligence for motor control systems. He has a background in Electrical and Electronics Engineering and has held various engineering roles in companies focused on embedded systems and quality assurance.
Work at Bosch Global Software Technologies
Rishab Kulkarni has been employed at Bosch Global Software Technologies as a Senior Engineer since 2022. His role involves significant contributions to automotive electrification, particularly specializing in 48V mild hybrid systems. He focuses on enhancing motor control systems through the development of artificial intelligence models. His work includes the deployment of TensorFlow models on microcontrollers, aimed at improving performance in automotive applications.
Education and Expertise
Rishab Kulkarni holds a Bachelor of Engineering (BE) in Electrical and Electronics Engineering from REVA University, where he studied from 2015 to 2019. He furthered his education by studying Python at Pentagon Space from 2020 to 2021. Additionally, he completed a course on MATLAB for Power Electronics: Simulation & Analysis through Udemy in 2020. His educational background supports his expertise in embedded systems and machine learning applications.
Background in Quality Assurance and Embedded Systems
Before his current role, Rishab worked at Kirloskar Electric Co. Ltd. as a Quality Assurance Engineer from 2019 to 2020. He also served as an Embedded System Engineer at inGO Electric Pvt Ltd for five months in 2020, followed by a position as a Technical Associate at Micelio Mobility from 2021 to 2022. His experience in these roles has equipped him with a solid foundation in quality assurance and embedded systems development.
Involvement in AI and Machine Learning Projects
Rishab Kulkarni has been actively involved in the development of motor AI models, focusing on enhancing motor control systems with artificial intelligence. He has benchmarked TensorFlow models using MATLAB to ensure their performance aligns with mathematical models. Furthermore, he has set up workflows for TinyML projects, integrating machine learning into embedded systems, showcasing his commitment to advancing technology in his field.