Led the development of an advanced Blender-based pipeline for synthetic data generation, significantly enhancing the robustness and generalizability of AI models across diverse applications, leading to more reliable and adaptable machine learning solutions.
Expertly leveraged foundation models and zero-shot learning techniques to elevate AI model performance in novel scenarios, optimizing generalizability and reliability, thereby advancing the frontier of machine learning applications in real-world contexts.
Deployed automation system for quality control to the existing production line identify glass print deflects and improve efficiency by 40%; deployed algorithm to identify defects in glass bottles with varying sizes and different types of glasses used.
Created a Python script to generate intelligence reports about prospective clients and potential sources for retail extracted from the international export databases based on their requirements production requirements based.
Creating a preprocessing pipeline using PySpark to enhance the data pre-processing speed by 20% while utilizing up to 30% of lesser resources; also improving the modularity of the existing code and increasing its efficiency
Created a database to identify the best location for electric vehicle charging location
Built a warning system to warn the vehicle owner about a possible failure based on data obtained from other consumers.
Hero Motor Corp
Summer Intern [July 2021 - August 2021]
Tech Stack: Python ,MS Office
Proposed a new packaging label design in compliance with Package Commodity Act
Performed packaging data analysis of the packaging data
Utilized Python for data correction and auto-identifying errors in the database
