Abhijoy Sarkar
About Abhijoy Sarkar
Abhijoy Sarkar is a Senior Machine Learning Scientist currently working at Multiverse Computing, where he applies his expertise in MLOps and Quantum Computing. He has a diverse background in software engineering and machine learning, with previous roles at Flipkart, Optum, Freelancer.com, and TheoremOne.
Work at Multiverse Computing
Abhijoy Sarkar currently serves as a Senior Machine Learning Scientist at Multiverse Computing, a position he has held since 2024. In this role, he applies his expertise in MLOps and Quantum Computing to enhance Large Language Models. His work focuses on driving innovation within the organization, utilizing advanced technologies to improve machine learning processes.
Previous Experience at Flipkart
Before joining Multiverse Computing, Abhijoy worked as a Software Engineer at Flipkart from 2021 to 2023. During his two-year tenure in Bengaluru, Karnataka, India, he contributed to various software development projects, gaining valuable experience in the tech industry.
Background in Machine Learning Research
Abhijoy has a solid background in machine learning research, having worked as a Machine Learning Researcher at Optum from 2018 to 2019. He also held the position of AI Researcher at TheoremOne in 2023, where he contributed to significant projects, including the development of the CodeMe project and an LLM Benchmarking Suite.
Education and Expertise
Abhijoy holds a Master of Technology (MTech) in Artificial Intelligence from the Indian Institute of Technology, Ropar, where he studied from 2019 to 2021. His educational background, combined with his expertise in MLOps and the MERN Stack, positions him as a knowledgeable professional in the fields of machine learning and software development.
Achievements in AI and Quantum Computing
Abhijoy has made notable contributions to the field of AI and Quantum Computing. At TheoremOne, he played a key role in the CodeMe project, which attracted a significant user base. He also contributed to the creation of an LLM Benchmarking Suite, achieving a 25% reduction in evaluation costs while enhancing the speed and accuracy of model assessments.