AI ENGINEER INTERN

• Reduced manual curation by 18 hours/week by architecting agentic AI system with React/Vite & RESTful APIs for autonomous content collection across 40+ data sources
• Engineered AI agents using Python for autonomous web scraping & BART transformer for NLP summarization, enabling weekly processing 280+ articles weekly with 92% relevance accuracy and 97% extraction rate.
• Implemented CI/CD ML pipeline using GitHub Actions for automated data ingestion, model inference, artifact deployment with 99.2%uptime, reducing deployment time by 87%.

AI/ML RESEARCH INTERN

• Developed preprocessing pipeline using Python, OpenCV, and NumPy on 1,200+ fundus images (CLAHE, denoising, vessel segmentation) to enhance diagnostic features.
• Trained CNN model in TensorFlow/PyTorch, achieving 82% accuracy and 0.78 F1-score for early-stage retinopathy detection, reducing manual workload.
• Built automated validation with cross-validation and Grad-CAM explainability, generating heatmaps to support ophthalmologist review.

DEEP LEARNING FOR CRIME PATTERN RECOGNITION, IEEE

January 2023 – May 2023

• Implemented XGBoost, CatBoost, TabNet, hybrid 1D CNN to predict crime patterns using Chicago, Boston & SF datasets.
• Achieved accuracy improvements using feature-oriented datasets, with XGBoost for Chicago 96.51% and San Francisco 26.50%.
• Demonstrated the effectiveness of the Bagging classifier for Boston, achieving the highest accuracy of 20.80%.

DETECTION OF INTOXICATION IN AUTOMOBILE DRIVERS, IEEE

July 2022 – November 2022

• Designed a CNN-based system to detect intoxication through ocular cues, using DL for better accuracy and real-time applicability.
• Evaluated VGG16, VGG19, MobileNet V2, ResNet50, & LSTM+Attention to identify the most effective model.
• Achieved highest validation accuracy with VGG16 86.72%.