Adebimpe M.

Key Skills

Software

Micro1

Surge AI

Remotasks

Top Subject Matter

Content moderation and product QA evaluation

AI-driven personalized learning recommendations and EdTech evaluation

LLM/chatbot response evaluation and labeling for training-data quality

Top Data Types

Text

Image

Video

Top Task Types

Classification

Prompt + Response Writing (SFT)

Polygon

Text Generation

Data Collection

Freelancer Overview

QA & Product Evaluation Specialist at ProtocolGemini. Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include N, A, and ChatGPT. AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

EdTech Product Testing & Curriculum QA Lead at Al-Halal Crypto Academy

TextClassification

Performed data annotation and quality validation for AI-driven personalized learning recommendation systems as part of end-to-end digital learning module QA. Turned structured participant feedback into actionable product improvement insights and used assessment frameworks to validate real learning effectiveness. Collaborated in evaluation cycles for curriculum content quality, accuracy, and learner experience. • Annotated and validated recommendation inputs/outputs quality checks • Designed measurement frameworks for learner satisfaction and effectiveness • Ran feedback cycles with large participant cohorts to inform labeling-driven improvements • Conducted benchmarking and gap analysis across EdTech platforms to guide evaluation standards

2020 - Present

AI Chatbot Performance Evaluation (Prompt Engineering & QA Focus) - Featured Project

TextPrompt + Response Writing (SFT)

Evaluated large numbers of AI chatbot responses for accuracy, relevance, safety, and tone consistency as part of an AI performance and QA initiative. Identified edge cases including hallucinated or biased outputs and used findings to improve training data quality. Created annotation guidelines adopted by a multi-person evaluation team to standardize response labeling and scoring. • Scored and reviewed 500+ chatbot outputs against rubric criteria • Labeled failure modes such as hallucinations and bias • Produced guidelines to ensure consistent evaluation across annotators • Used results to improve training-data quality signals for downstream learning

2024 - 2024

QA & Product Evaluation Specialist at ProtocolGemini

Text

Performed manual QA data collection by executing test cases and logging defects with reproduction steps and severity ratings for product evaluation. Maintained high consistency in content moderation QA by applying platform guidelines during review. Coordinated with product and engineering to triage issues, verify fixes, and validate releases in staging. • Executed structured test runs and defect reporting workflows • Applied severity/rating criteria to observed moderation outcomes • Trained new team members on QA protocols to improve detection quality • Streamlined tester–developer feedback loops to reduce resolution time

2022 - 2024

Education

Work History