I have over 5 years of experience in AI quality assurance and data annotation. My roles have included Agent Evaluation Engineer and AI Data Annotation Specialist. I am proficient in labeling and validating datasets, reviewing AI-generated outputs, and enforcing quality standards. I have expertise in ensuring accuracy, safety, and compliance with guidelines in AI training projects. I am skilled at providing actionable feedback to enhance AI system performance. I have experience working remotely and independently, with strong attention to detail and excellent English reading comprehension. I have industry experience in artificial intelligence and machine learning.…

Helen Badmus

I have over 5 years of experience in AI quality assurance and data annotation. My roles have included Agent Evaluation Engineer and AI Data Annotation Specialist. I am proficient in labeling and validating datasets, reviewing AI-generated outputs, and enforcing quality standards. I have expertise in ensuring accuracy, safety, and compliance with guidelines in AI training projects. I am skilled at providing actionable feedback to enhance AI system performance. I have experience working remotely and independently, with strong attention to detail and excellent English reading comprehension. I have industry experience in artificial intelligence and machine learning.…

Available to hire

I have over 5 years of experience in AI quality assurance and data annotation. My roles have included Agent Evaluation Engineer and AI Data Annotation Specialist. I am proficient in labeling and validating datasets, reviewing AI-generated outputs, and enforcing quality standards.

I have expertise in ensuring accuracy, safety, and compliance with guidelines in AI training projects. I am skilled at providing actionable feedback to enhance AI system performance. I have experience working remotely and independently, with strong attention to detail and excellent English reading comprehension. I have industry experience in artificial intelligence and machine learning.

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Skills

Experience Level

Language

English
Fluent
Yoruba
Fluent

Work Experience

AI Data annotation specialist at AtlasCapture AI
January 1, 2025 - Present
Labeled and validated text and document datasets for AI projects. Ensured accuracy, completeness, and compliance with data privacy standards. Conducted quality checks to maintain high data quality for AI training.
AI Data Annotation & QA specialist at Scale AI
March 1, 2022 - March 1, 2025
Annotated and evaluated datasets for AI training and model improvement. Reviewed AI outputs for accuracy, safety, and guideline compliance. Conducted quality assurance checks and flagged errors or inconsistencies.
Freelance Content Reviewer and Researcher at Upwork
January 1, 2019 - Present
Reviewed and edited content with attention to clarity, accuracy, and compliance. Achieved 95% job success rate with consistent five-star client reviews.

Education

Bachelor's degree at KWARA STATE UNIVERSITY
January 1, 2019 - January 1, 2023
Master's degree at KWARA STATE UNIVERSITY
January 1, 2023 - April 12, 2026

Qualifications

Add your qualifications or awards here.

Industry Experience

Software & Internet, Professional Services, Other
    paper High-Quality Data Annotation for Machine Learning Training

    This project involved preparing and labeling structured datasets for machine learning model training. The goal was to ensure high-quality, consistent, and accurately annotated data that can be used to improve the performance of AI systems.

    I worked with both image and text-based datasets, applying clear labeling guidelines to ensure consistency, relevance, and precision across all entries. Each data point was carefully reviewed to reduce noise, eliminate ambiguity, and maintain dataset integrity.

    Key responsibilities included:

    Annotating and labeling image and text data according to defined guidelines

    Ensuring consistency and accuracy across all labeled datasets

    Identifying and correcting ambiguous or incorrectly tagged data

    Maintaining high standards of data quality for machine learning use cases

    Structuring datasets for improved model training efficiency

    Outcome:
    The final annotated datasets were clean, consistent, and optimized for machine learning model training, improving their usability for supervised learning tasks and reducing potential errors in model outputs.

    paper AI Response Evaluation and Quality Assurance

    Project overview
    This project involved evaluating AI-generated responses for accuracy, clarity, coherence, and overall quality. The goal was to identify weaknesses in model outputs and improve them to meet human-level communication standards.

    Approach
    I reviewed multiple AI responses across different prompts, assessing them for factual correctness, logical consistency, tone appropriateness, and grammatical accuracy. Where necessary, I rewrote responses to improve structure, reasoning, and readability.

    Key Responsibilities
    Detected factual errors and logical inconsistencies in AI outputs
    Improved clarity, coherence, and readability of responses
    Ensured tone alignment with user intent and context
    Rewrote and refined outputs for better logical flow and precision

    Outcome
    The refined responses showed improved accuracy, stronger reasoning, and more natural communication, making them better aligned with professional AI training and evaluation standards.