
AI/ML engineer skilled in deep learning, NLP, and computer vision. Experienced in hackathons, research, and building real-time intelligent systems .
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Retail_Inventory_Pipeline
April 22, 2026 – Present
Retail_Inventory_Pipeline — GitHub repository
View ProjectForced-Alignment-using-Montreal-Forced-Aligner-MFA-
February 8, 2026 – Present
Forced-Alignment-using-Montreal-Forced-Aligner-MFA- — GitHub repository
View ProjectPPE_Detection_YOLOv8
November 26, 2025 – November 26, 2025
PPE_Detection_YOLOv8 — GitHub repository
View ProjectDiscCupVessel-MultiTask-Seg
November 25, 2025 – November 25, 2025
Deep Learning Pipeline: A U-Net–based system for glaucoma detection using fundus images. Tasks: • Vessel segmentation (256×256 patches ×4/image) → vessel density. • Optic disc (OD) segmentation → CDR computation. Data: ~12K multi-modal images with age, IOP, VCDR. Tech: PyTorch/Keras, Pandas, Matplotlib; supports hybrid vision+clinical models.
View ProjectAquatect
October 25, 2023 – November 26, 2025
Created and optimized an ensemble learning model that combines the power of Support Vector Machine (SVM), k-Nearest Neighbors, Linear Regression, Decision Tree, and Random Forest models, with Linear Regression as the central meta-model. This ensemble approach significantly enhanced the accuracy of reservoir inflow predictions, contributing to more.
View ProjectCultural Fit Analysis
The candidate's projects are primarily personal and demonstrate a strong interest in machine learning and deep learning. However, the lack of professional experience or team-based projects makes it difficult to assess cultural fit comprehensively. The project descriptions are brief, limiting insight into collaboration or problem-solving approaches. The target role is Data Scientist, and the projects align well with the technical aspects of this role, particularly in applied machine learning and computer vision.
Soft Skills & Operational Fit
Insufficient data to assess soft skills or operational fit. No psychometric test results or interview feedback provided.