Prabhtaj Singh Bhasin
About Prabhtaj Singh Bhasin
Prabhtaj Singh Bhasin is a Machine Learning Engineer III with a strong background in software engineering and machine learning applications. He has worked at notable companies including Johnson & Johnson and Accolite, and currently holds a position at Turing.
Current Role at Turing
Prabhtaj Singh Bhasin is currently employed as a Machine Learning Engineer - III at Turing, a position he has held since 2024. He works remotely and has been in this role for approximately seven months. His responsibilities include developing and implementing machine learning models and solutions.
Previous Experience at Johnson & Johnson
Before joining Turing, Prabhtaj worked at Johnson & Johnson as a Machine Learning Engineer from 2022 to 2023. His role was based in Sahibzada Ajit Singh Nagar, Punjab, India, and he operated in a remote capacity. His work involved applying machine learning techniques to various projects.
Experience at Accolite
Prabhtaj Singh Bhasin spent two years at Accolite, where he held multiple positions. He served as a Senior Software Engineer specializing in ML/AI from 2020 to 2022 and as a Software Engineer from 2019 to 2020. He also interned at Accolite in 2018 for six months. His work included developing and deploying machine learning models and contributing to various AI projects.
Educational Background
Prabhtaj completed his Master of Computer Applications (MCA) at Savitribai Phule Pune University from 2016 to 2019. He also holds a Bachelor of Computer Applications (BCA) degree from Goswami Ganesh Dutta SD College, where he studied from 2013 to 2016. His education provided a strong foundation in computer science and software engineering.
Technical Skills and Projects
Prabhtaj possesses skills in FastAPI, Azure Kubernetes Service, and Django, indicating a robust background in software engineering. He has practical experience in developing computer vision models for applications such as e-KYC, ID-OCR, and facial recognition. He contributed to the AI Smart Inspection project, which significantly reduced inspection times by 90% and saved over 10,000 manual work hours annually.