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Nada Shaaban

@nadashaaban

AI / Machine Learning Engineer at Drop Me who built a bilingual customer assistant and deployed a vision pipeline on a reverse vending machine.

Egypt

Message

At Drop Me, I built an Arabic and English customer assistant for app navigation, recycling guidance, and rewards questions, with safety guardrails and an evaluation workflow of 885 automated test cases.

I also deployed a YOLOv8 and DINOv2 pipeline on a reverse vending machine, achieving 90.2% end-to-end correctness in offline evaluation. I built AWS data infrastructure to turn machine AI outputs into structured analytical data.

Experience

Work history, roles, and key accomplishments

AI & Business Intelligence Engineer

Drop Me

Dec 2025 - Sep 2026 (9 months)

Built an Arabic and English customer assistant using FastAPI, Gemini, and API tool calling, and developed a chatbot evaluation workflow with 885 automated test cases. Deployed a YOLOv8 and DINOv2 pipeline on a reverse vending machine, achieving 90.2% end-to-end correctness, and built AWS data infrastructure for machine AI outputs.

fastAPI Gemini Tool calling GuardRails YOLOv8 DinoV2 AWS RDS Lambda S3 EventBridge Python Model Evaluation

AI Engineer Intern

Drop Me

Sep 2025 - Dec 2025 (3 months)

Developed YOLOv8 detection and evaluated EfficientNet-B3 classification for item acceptance, reaching 0.75 mAP@0.5:0.95. Optimized CPU inference using OpenVINO and ONNX, reducing latency to approximately 170 ms per item, and built a beverage brand recognition workflow using clustering and K-nearest neighbors.

YOLOv8 EfficientNet B3 OpenVINO Onnx Clustering K Nearest Neighbors Object Detection Model Evaluation