About Harmonia | Our Mission for AI Workforce Solutions

Ayesha

Lead UI/UX Designer

Chinedum Okike

Strategic Advisor

Chinedum Okike

AI trainer with a comprehensive background spanning data annotation, quality assurance, and real-world model training. Experienced across the full AI data pipeline—from capturing and labeling training content to evaluating model outputs and improving annotation workflows at scale.

Chinedum brings a QA engineering discipline to AI training, ensuring accuracy holds up under scrutiny. He has annotated over 5,000 minutes of video content with a sustained 95% quality score, trained 40+ annotators, and was inducted into the Atlas AI Hall of Fame. He has also contributed 100+ hours of egocentric video data for spatial reasoning AI models. He holds a B.Sc. in Computer Science from the University of Lagos.

Core Competencies

Critical Analysis AI Output Evaluation Model Behavior Assessment Attention to Detail Prompt Engineering & Review Defect Identification & Reporting Process Standardization & QA SOPs

Professional Experience

AI Data Annotator | Atlas AI (2025–Present)

Annotated over 5,000 minutes of video content with a sustained quality score of 95%, consistently exceeding team accuracy benchmarks.
Onboarded and trained 40+ annotators from diverse backgrounds, developing structured onboarding materials that reduced ramp-up time by approximately 30%.
Audited existing annotation workflows and implemented targeted process improvements that increased overall data annotation quality by 22%.
Recognized as a Spotlight Annotator and inducted onto the Hall of Fame for exceptional accuracy, throughput, and contributions to team performance.

AI Video Data Contributor (2026–Present)

Recorded 100+ hours of first-person perspective video across 15+ real-world task categories specifically designed to support spatial reasoning and visual understanding in AI systems.
Applied granular timestamp-based labeling and rich natural-language scene descriptions to each video, significantly enhancing metadata quality for downstream model training.
Independently scoped, planned, and executed multi-category recording sessions to ensure diverse, bias-mitigating coverage of everyday human tasks and environments.

Quality Assurance Engineer | TesterWorld (2023–2025)

Identified and reported 70+ inconsistencies across AI-integrated tools and platforms, providing structured reproduction steps and severity classifications that accelerated resolution cycles.
Introduced standardized defect reporting templates across the product team, reducing Mean Time to Resolution (MTTR) for critical bugs by 40% within two quarters.
Awarded QA Tester of the Month for outstanding defect detection rate and the measurable operational improvements driven by process standardization initiatives.

Skills & Tools

CVAT Label Studio Scale AI LLM output review RLHF/RLAIF feedback Exploratory & Regression Testing Jira & Confluence

Education

B.Sc. Computer Science | University of Lagos | 2022
Google Machine Learning Course | 2025

Gabriel John

Support Coordinator

Hope Gabriel

Client Success Manager

Ogor Ovie (Ovie)

Community Operations Manager

Ogor Oghenerukevwe Ovie

Technical professional specializing in video labeling, LLM evaluation, and function calling assessment. Combines a strong Mechanical Engineering background with hands-on expertise in computer vision pipelines and AI systems evaluation.

Ovie is a highly versatile contributor who bridges engineering fundamentals with AI systems. At Scale AI, he labeled 2,500+ data points daily with 99.2% accuracy, ranking in the top 5% of annotators. He has built custom AI-integrated analysis pipelines in Python and designed a drone flight-test analysis tool using OpenCV. He holds a B.Eng. in Mechanical Engineering and dual SolidWorks certifications.