Abdullah Shah
About Abdullah Shah
Abdullah Shah is a Data Engineer currently working at Bristlecone in Bengaluru, India, where he has contributed significantly to various projects, including the migration of 'doc.ai' from AWS to GCP. He holds a Bachelor of Technology in Computer Science and Engineering from the University of Kashmir and has experience in machine learning and forecasting models.
Work at Bristlecone
Abdullah Shah has been employed at Bristlecone as a Data Engineer since 2022. In this role, he has been involved in various data engineering projects, contributing to the development of cloud functions and APIs. His work focuses on enhancing data functionality and efficiency, particularly in the context of large-scale manufacturers and IT companies. Prior to his current position, he served as an Associate Consultant at Bristlecone for six months in 2021.
Education and Expertise
Abdullah Shah earned a Bachelor of Technology (BTech) degree in Computer Science and Engineering from the University of Kashmir, completing his studies from 2017 to 2021. His educational background provides a strong foundation in data engineering principles and practices. He has applied his academic knowledge in practical settings, particularly in projects involving machine learning and time series forecasting.
Background
Abdullah Shah is based in Bengaluru, Karnataka, India. His career in data engineering began with significant contributions to the migration of 'doc.ai' from AWS to GCP. He played a crucial role in developing cloud functions and APIs, utilizing advanced technologies such as Google Cloud's Document AI model and AWS Textract for optical character recognition (OCR).
Achievements
Abdullah Shah has contributed over 50,000 lines of code to improve the functionality and efficiency of 'doc.ai'. He has managed multiple Proof of Concepts for large-scale manufacturers and IT companies. His work on the Grapes Price Prediction project involved developing predictive models that incorporated historical data, leading to enhanced accuracy in forecasting. Additionally, he played a key role in an ML-based Time Series Forecasting project, achieving a 40% increase in demand forecast accuracy.