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James Akedi

James Akedi

AI Data Annotation & Labeling Professional

Kenya flagNairobi, Kenya
$10.00/hrIntermediateOtherInternal Proprietary Tooling

Key Skills

Software

Other
Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

AudioAudio
ImageImage
VideoVideo

Top Task Types

Audio Recording
Classification
Data Collection
Emotion Recognition
Evaluation Rating

Freelancer Overview

I am an experienced Data Labeling and AI Training Data Specialist with hands-on expertise gained through my work at RWS on Meta’s Diamond and Ruby Projects. My role involved high-accuracy annotation, quality control, and data enrichment across complex datasets used to train and refine machine learning models. I have worked extensively with image, text, and multimedia data, ensuring precision, consistency, and adherence to project-specific taxonomies and quality metrics. I bring strong analytical and linguistic skills, deep attention to detail, and familiarity with large-scale annotation tools and workflows. My background in collaborating on global AI initiatives has honed my ability to meet stringent accuracy targets and deliver clean, AI-ready datasets that improve model performance. I thrive in fast-paced, quality-driven environments and am passionate about contributing to the development of reliable, human-centered AI systems.

IntermediateSwahiliEnglish

Labeling Experience

Ruby

Internal Proprietary ToolingImageBounding BoxEntity Ner Classification
The Ruby Project by RWS, conducted for Meta, focused on high-precision image and video data labeling, particularly involving face comparison and facial recognition tasks. Annotators worked on identifying, matching, and labeling facial features across image pairs or sequences, supporting the development of advanced computer vision and AI models related to identity matching and visual consistency. The project required careful attention to facial details, angles, lighting variations, and expressions to ensure accurate tagging and comparison across large datasets. The scope and scale of Ruby were substantial, involving thousands of image pairs processed by globally distributed annotators through RWS’s TrainAI (Parimango) platform. Strict quality control measures were enforced, including the use of gold-standard reference samples, regular accuracy checks, peer reviews, and inter-annotator agreement tracking. Annotators were required to maintain accuracy levels above 95%

The Ruby Project by RWS, conducted for Meta, focused on high-precision image and video data labeling, particularly involving face comparison and facial recognition tasks. Annotators worked on identifying, matching, and labeling facial features across image pairs or sequences, supporting the development of advanced computer vision and AI models related to identity matching and visual consistency. The project required careful attention to facial details, angles, lighting variations, and expressions to ensure accurate tagging and comparison across large datasets. The scope and scale of Ruby were substantial, involving thousands of image pairs processed by globally distributed annotators through RWS’s TrainAI (Parimango) platform. Strict quality control measures were enforced, including the use of gold-standard reference samples, regular accuracy checks, peer reviews, and inter-annotator agreement tracking. Annotators were required to maintain accuracy levels above 95%

2025 - 2025

Diamond

Internal Proprietary ToolingVideoBounding BoxClassification
Meta’s Diamond Project, delivered through RWS, focused on creating large, high-quality datasets to train and refine machine learning and AI systems. The project involved large-scale data labeling across image, video, and possibly text modalities. Tasks typically included object detection and segmentation, image and scene classification, transcription, and semantic labeling. Annotators also participated in refining labeling guidelines, handling complex or ambiguous cases, and ensuring consistent application of Meta’s taxonomy and data standards across thousands of samples. To maintain data integrity and model reliability, the project adhered to strict quality assurance protocols. These included inter-annotator agreement checks, accuracy benchmarks often above 95%, spot audits, and the use of gold-standard reference data for calibration. Continuous feedback loops, guideline updates, and performance tracking ensured both precision and efficiency.

Meta’s Diamond Project, delivered through RWS, focused on creating large, high-quality datasets to train and refine machine learning and AI systems. The project involved large-scale data labeling across image, video, and possibly text modalities. Tasks typically included object detection and segmentation, image and scene classification, transcription, and semantic labeling. Annotators also participated in refining labeling guidelines, handling complex or ambiguous cases, and ensuring consistent application of Meta’s taxonomy and data standards across thousands of samples. To maintain data integrity and model reliability, the project adhered to strict quality assurance protocols. These included inter-annotator agreement checks, accuracy benchmarks often above 95%, spot audits, and the use of gold-standard reference data for calibration. Continuous feedback loops, guideline updates, and performance tracking ensured both precision and efficiency.

2025 - 2025

Education

E

Egerton University

Master of Arts, Sociology

Master of Arts
2023 - 2025
U

University of Nairobi

Bachelor of Arts, Economics & Sociology

Bachelor of Arts
2003 - 2011

Work History

R

RWS Global

AI Data Annotation Specialist

Nairobi
2023 - Present
W

World Climate Corps

Strategic Sustainability Consultant

Nairobi
2023 - Present