Sameen Islam
About Sameen Islam
Sameen Islam is a Software Engineer specializing in AI, currently employed at Provenir in London since 2022. He holds a Master of Science in Artificial Intelligence from the University of Southampton and has previous experience at companies such as Accenture and Airbus Defence and Space.
Work at Provenir
Sameen Islam has been working at Provenir as a Software Engineer specializing in Artificial Intelligence since 2022. Based in London, England, he operates in a remote capacity. In this role, he has taken ownership of various projects focused on feature extraction from fraud networks, providing clients with 'feature as a service'. His responsibilities also include presenting software demonstrations to senior executives, showcasing the capabilities and advancements of the software developed.
Education and Expertise
Sameen Islam holds a Master of Science (MSc) degree in Artificial Intelligence from the University of Southampton, which he completed from 2020 to 2021. He also earned a Bachelor of Science (BSc) in Computer Science from Queen Mary University of London, studying there from 2014 to 2018. His educational background includes A Levels in Further Maths, Maths, Physics, and Computer Science from Townley Grammar School, completed between 2012 and 2014.
Background
Sameen Islam's professional journey includes various roles in the technology sector. He worked as a System Developer Analyst at Accenture for nine months in 2018 and briefly served as a Junior Computer Vision Engineer at Disperse in 2022. His experience also includes a position as a Spacecraft Database Developer at Airbus Defence and Space - Intelligence from 2016 to 2017. Additionally, he participated in a work experience program at HSBC in 2008.
Achievements
During his career, Sameen Islam has contributed to significant projects and initiatives. He mentored the Data Science team in Software Engineering principles, which improved collaboration and quality. He performed code profiling and ran experiments to establish performance benchmarks of various graph libraries, identifying bottlenecks and optimizing code. Notably, he created an improved version of an existing graph processing workflow that significantly reduced processing time from 40 seconds to 350 milliseconds.