Faysal Ishtiaq Rabby
About Faysal Ishtiaq Rabby
Faysal Ishtiaq Rabby is an MLOps Manager at Multiverse Computing, where he leads a team focused on operational applications and research in quantum computing and machine learning. He has a background in software engineering and has contributed to various projects using technologies such as Python, AWS, and Kubernetes.
Current Role as MLOps Manager
Faysal Ishtiaq Rabby currently serves as an MLOps Manager at Multiverse Computing, a position he has held since 2024. He works in a hybrid capacity from San Sebastián, Basque Country, Spain. In this role, he leads a team of MLOps Engineers and Data Engineers, focusing on the maintenance of operational applications and research that utilizes quantum computing and machine learning.
Previous Experience at Multiverse Computing
Prior to his current role, Faysal worked at Multiverse Computing as a Senior Software Engineer from 2023 to 2024. During his tenure, he contributed to the development of distributed backend systems using a microservice approach and Kafka as an event streaming service. He also held a position as a Software Engineer at the same company from 2022 to 2023.
Education and Academic Background
Faysal completed his Bachelor of Science (BSc) in Electronics and Communications Engineering at Khulna University from 2012 to 2016. He also attended Bogra Cantonment Public School And College, where he studied Science from 2004 to 2011. His educational background provides a strong foundation for his work in software engineering and MLOps.
Technical Skills and Contributions
Faysal specializes in developing and deploying scalable software solutions. His technical expertise includes Python programming, infrastructure as code tools, and cloud technologies such as AWS services and Kubernetes. He has also worked as a freelance software engineer on Upwork, developing distributed backend systems.
Research and Publications
Faysal co-authored a paper on quantum-enhanced machine learning for credit rating prediction, which was published in Physical Review Research. This contribution highlights his involvement in advanced research at the intersection of quantum computing and machine learning.