Kibugi Kamau Mbugua
About Kibugi Kamau Mbugua
Kibugi Kamau Mbugua is a Software Engineer with experience in AI model development and data management. He currently works at Ponea Health in Nairobi, Kenya, and has previously held positions at eDataFarm LLC, Drew University, and Coders Data.
Current Role at Ponea Health
Kibugi Kamau Mbugua has been employed as a Software Engineer at Ponea Health since 2021. His role is based in Nairobi, Kenya, where he has contributed to various software development projects for three years. His work focuses on enhancing health technology solutions through software engineering.
Experience at Turing
Since 2022, Kibugi has also worked as a Software Engineer at Turing. This position allows him to engage in remote software development, further expanding his expertise in the field. His responsibilities include contributing to projects that require advanced programming skills.
Previous Employment History
Prior to his current roles, Kibugi worked at eDataFarm LLC as a Data Collections Manager for four months in 2020. He also served as a Computer Science Tutor at Drew University from 2018 to 2019 and as an Information Technology Support Specialist at the same institution for nine months. Additionally, he worked as a Full Stack Web Developer at Coders Data for eight months in 2019.
Educational Background
Kibugi Kamau Mbugua studied at Drew University, where he earned a Bachelor of Arts in Computer Science from 2017 to 2019. He also completed an Undergraduate Certificate of Higher Education at The University of Edinburgh from 2014 to 2016. His educational pursuits include courses in Artificial Intelligence and Computer Architecture at Columbia University in 2018.
Technical Skills and Contributions
Kibugi has played a significant role in data cleaning, modeling, and presentation to enhance AI model performance. He has contributed to the development of AI models by training them to address complex programming, system design, and debugging challenges using Python. His collaborative efforts have focused on innovating new learning materials to improve AI model autonomy.