Ahmed Fattachi

Key Skills

Software

Labelbox

Scale AI

CVAT

Top Subject Matter

Computer Vision

French Dialect Recognition

Urban Imagery/Architectural Shapes

Top Data Types

Video

Audio

Image

Top Task Types

Tracking

Classification

Bounding Box

Freelancer Overview

AI Content Specialist & Annotator. Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Labelbox, Scale AI, and CVAT. Education includes Master of Management, École de Commerce de Lyon (2024). AI-training focus includes data types such as Video, Audio, and Image and labeling workflows including Tracking, Classification, and Bounding Box.

Labeling Experience

AI Content Specialist & Annotator

LabelboxVideoTracking

I provided high-precision video annotations specializing in object tracking and image categorization for computer vision projects. My work focused on linguistic and cultural accuracy for both English and French datasets, consistently maintaining a 98% quality score. Guideline adherence and collaboration with international QA teams were key parts of my methodology. • Specialized in video object tracking using advanced annotation protocols. • Responsible for image categorization tasks across multimodal datasets. • Evaluated, rated, and improved LLM responses for safety, accuracy, and relevance. • Utilized multiple platforms to deliver top-quality annotations and edge case resolutions.

I provided high-precision video annotations specializing in object tracking and image categorization for computer vision projects. My work focused on linguistic and cultural accuracy for both English and French datasets, consistently maintaining a 98% quality score. Guideline adherence and collaboration with international QA teams were key parts of my methodology. • Specialized in video object tracking using advanced annotation protocols. • Responsible for image categorization tasks across multimodal datasets. • Evaluated, rated, and improved LLM responses for safety, accuracy, and relevance. • Utilized multiple platforms to deliver top-quality annotations and edge case resolutions.

2023 - Present

Geometric Image Mapping Project – Data Annotator

CVATImageBounding Box

I developed and labeled urban images by assigning bounding boxes to various architectural shapes in city photography. Each annotation required careful attention to detail and a consistent labeling schema. The goal was to advance object detection and classification for geometric urban features. • Mapped geometric shapes using visual segmentation strategies. • Ensured uniform, high-quality labeling throughout the project. • Utilized CVAT for technical annotation workflows. • Generated a unique dataset to support architectural AI research.

I developed and labeled urban images by assigning bounding boxes to various architectural shapes in city photography. Each annotation required careful attention to detail and a consistent labeling schema. The goal was to advance object detection and classification for geometric urban features. • Mapped geometric shapes using visual segmentation strategies. • Ensured uniform, high-quality labeling throughout the project. • Utilized CVAT for technical annotation workflows. • Generated a unique dataset to support architectural AI research.

2022 - 2022

French Dialect Recognition Project – Data Annotator

Scale AIAudioClassification

I labeled more than 200 hours of French audio to assist an AI model in distinguishing regional accents. My work emphasized linguistic nuance and careful rubric adherence for each sample. This improved the underlying model's dialect recognition accuracy. • Applied deep phonetic and acoustic analysis techniques. • Followed complex guidelines to ensure high-quality annotation consistency. • Collaborated with language specialists to validate annotated segments. • Enhanced AI model adaptability for diverse French language inputs.

I labeled more than 200 hours of French audio to assist an AI model in distinguishing regional accents. My work emphasized linguistic nuance and careful rubric adherence for each sample. This improved the underlying model's dialect recognition accuracy. • Applied deep phonetic and acoustic analysis techniques. • Followed complex guidelines to ensure high-quality annotation consistency. • Collaborated with language specialists to validate annotated segments. • Enhanced AI model adaptability for diverse French language inputs.

2022 - 2022

Education

Work History