Egbon Emmanuel

Key Skills

Software

Appen

Axiom AI

CVAT

Data Annotation Tech

Diffgram

Doccano

Labelbox

Label Studio

Prodigy

Top Subject Matter

AI Task Operation and Data Labeling

AI Data Labeling Projects

Top Data Types

Image

Medical Dicom

Video

Text

Top Label Types

Action Recognition

Evaluation Rating

Object Detection

Point Key Point

Text Generation

Freelancer Overview

AI Task Auditor / Data Labeling Contributor — Atlas Capture. Brings 10+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal and Proprietary Tooling. Education includes Bachelor of Science, BYU Pathway Worldwide (2025) and Senior School Certificate, N/A (2015). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Labeling Experience

AI Data Project Contributor (Community & Project Involvement)

Text

I contributed to global AI data projects, collaborating with international remote teams. My involvement included assisting peers with onboarding and interpretation of annotation guidelines. I participated in various labeling environments to help drive successful project completion. • Supported peers in data labeling projects and guideline interpretation. • Facilitated collaboration among a global, distributed team. • Participated in professional AI data and labeling communities. • Ensured proper onboarding for new contributors in AI labeling tasks.

I contributed to global AI data projects, collaborating with international remote teams. My involvement included assisting peers with onboarding and interpretation of annotation guidelines. I participated in various labeling environments to help drive successful project completion. • Supported peers in data labeling projects and guideline interpretation. • Facilitated collaboration among a global, distributed team. • Participated in professional AI data and labeling communities. • Ensured proper onboarding for new contributors in AI labeling tasks.

2025 - Present

AI Task Auditor / Data Labeling Contributor — Atlas Capture

Text

As an AI Task Auditor and Data Labeling Contributor, I was responsible for reviewing and auditing labeling outputs for quality and accuracy. I supported contributors by clarifying complex guidelines and sharing feedback on edge cases. My role required operational discipline and close collaboration with global distributed teams. • Reviewed AI labeling outputs for accuracy and compliance with guidelines. • Shared clear, actionable feedback to help contributors improve quality. • Worked with quality assurance (QA) and review teams across multiple projects. • Used internal online tools and dashboards to manage audit workflow.

As an AI Task Auditor and Data Labeling Contributor, I was responsible for reviewing and auditing labeling outputs for quality and accuracy. I supported contributors by clarifying complex guidelines and sharing feedback on edge cases. My role required operational discipline and close collaboration with global distributed teams. • Reviewed AI labeling outputs for accuracy and compliance with guidelines. • Shared clear, actionable feedback to help contributors improve quality. • Worked with quality assurance (QA) and review teams across multiple projects. • Used internal online tools and dashboards to manage audit workflow.

2025 - Present

AI Data Labeling & Task Evaluation Project

AppenTextClassificationText Summarization

This project involved reviewing, labeling, and auditing text-based AI training data to improve the accuracy and reliability of large language models. My responsibilities included evaluating AI-generated responses against detailed guidelines, classifying outputs by intent and quality, and identifying errors, bias, or inconsistencies. I also performed fact-checking through web research to validate claims and ensure responses were supported by credible sources. The work required high attention to detail, consistency across large volumes of tasks, and the ability to apply nuanced judgment rather than simple rule-based decisions. Quality was maintained by strictly following annotation guidelines, double-checking outputs, and providing clear feedback to support model improvement.

This project involved reviewing, labeling, and auditing text-based AI training data to improve the accuracy and reliability of large language models. My responsibilities included evaluating AI-generated responses against detailed guidelines, classifying outputs by intent and quality, and identifying errors, bias, or inconsistencies. I also performed fact-checking through web research to validate claims and ensure responses were supported by credible sources. The work required high attention to detail, consistency across large volumes of tasks, and the ability to apply nuanced judgment rather than simple rule-based decisions. Quality was maintained by strictly following annotation guidelines, double-checking outputs, and providing clear feedback to support model improvement.

2025

Education

Work History