Deep Learning Intern (Computer Vision) at Tiama

Conducted advanced object detection experiments using deep learning algorithms like YOLO v6 with Mobilenet backbone, enhancing defect identification in glass products. Developed a custom heuristic algorithm in Python to solve the Multichoice-Knapsack problem, optimizing dataset partitioning and boosting model training efficiency. Increased the IoU metric by 17% and reduced false negatives, addressing critical issues in defect detection. Collaborated with cross-functional teams to translate complex technical insights into actionable business strategies.

AI R&D Engineering Intern at ST Microelectronics

Led an advanced R&D initiative integrating AI-driven solutions into plasma technology for precision etching, with successful implementation during the sensing phase. Developed an ensemble-based regression framework leveraging boosting models (XGBoost, AdaBoost, etc.) for predictive accuracy. Applied nested cross-validation and grid search for hyperparameter optimization. Deployed a prototype ML workflow on AWS SageMaker to validate model performance and assess prediction consistency between offline training and production environments.

Programmer Analyst at Cognizant Technology Solutions

Developed and maintained analytical datasets using Python in Big Data environments, processing large-scale manufacturing and operational data to support ML model development and business analytics. Designed ETL workflows using Informatica PowerCenter and Talend, orchestrating data extraction, transformation, and loading across multiple sources into PostgreSQL data warehouses. Implemented query optimization and indexing strategies to enhance retrieval performance for downstream analytics. Leveraged PySpark for distributed data processing, improving pipelines scalability and performance when handling high-volume sensor and production datasets. Refactored legacy codebase to modern C++ standards to enable integration of new algorithms into production.