Mukezangoma J.

Key Skills

Software

Clickworker

Appen

CVAT

Data Annotation Tech

Micro1

Mindrift

Scale AI

Surge AI

Toloka

Top Subject Matter

AI video/audio content evaluation for training datasets

AI boundary detection dataset annotation

Top Data Types

Video

Image

Audio

Top Task Types

Segmentation

Bounding Box

Text Generation

RLHF

Text Summarization

Transcription

Data Collection

Prompt + Response Writing (SFT)

Freelancer Overview

AI Video & Audio Evaluation Project - Invisible Technologies. Brings 2+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Other and Feather. Education includes Technical Secondary School Diploma, Technical Secondary School - Road Construction, IPRC Kigali (2024) and Ordinary Level Diploma, Lycée de Kigali (2021). AI-training focus includes data types such as Video and labeling workflows including Evaluation, Rating, and Segmentation.

Labeling Experience

AI Boundary Detection Annotation Project - Invisible Technologies

VideoSegmentation

You annotated and reviewed datasets for AI boundary detection tasks to improve machine learning training accuracy. You performed labeling work using remote productivity and tracking tools and ensured outputs adhered to project requirements. Your contributions supported accurate model learning for boundary detection use cases. • Produced boundary detection annotations for training data • Reviewed and validated labeled datasets for consistency • Managed remote workflow with productivity tracking tools • Used annotation platform workflows for dataset generation

2025 - 2025

AI Video & Audio Evaluation Project - Invisible Technologies

OtherVideo

You evaluated video and audio artifacts to determine whether content is AI generated or natural components. You reviewed multimedia outputs to verify quality and accuracy for AI training datasets while strictly following annotation guidelines. You ensured labeled outputs met dataset requirements for model training use cases. • Assessed AI vs natural characteristics in video/audio artifacts • Verified multimedia quality for training dataset suitability • Followed strict labeling guidelines and QA expectations • Prepared evaluated results for AI training consumption

2025 - 2025

Education

Work History