Abdulmuheez Abdulkareem

Key Skills

Software

Clickworker

Data Annotation Tech

Scale AI

Argilla

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Text

Top Label Types

Prompt Response Writing SFT

Question Answering

RLHF

Text Summarization

Transcription

Freelancer Overview

I am a senior AI trainer and scientific LLM evaluator with over four years of hands-on experience in large language model testing, data annotation, and prompt engineering, particularly within biomedical, toxicology, and life sciences domains. My work includes over 3,000 hours of structured LLM evaluation, where I have designed and refined more than 1,200 high-complexity prompts, conducted adversarial testing, and systematically benchmarked models like ChatGPT, Claude, and Gemini for accuracy, reasoning stability, and hallucination detection. I specialize in applying rigorous grading frameworks, statistical validation, and cross-model analysis to ensure high-quality, reliable AI outputs. My background in veterinary medicine and ongoing MSc in toxicology enable me to bring domain expertise to data labeling, annotation, and quality assurance, ensuring outputs are evidence-based, logically sound, and aligned with scientific standards. I am highly proficient with tools such as IBM SPSS, GraphPad Prism, and various bioinformatics platforms, and have a strong track record in improving data quality and supporting AI model alignment in complex scientific contexts.

Labeling Experience

AI Voice Data Annotation & Speech Quality Specialist

ArgillaAudioText SummarizationAudio Recording

I am involved I transcription validation, pronunciation accuracy assessment and grading of AI generated voice outputs. Executed comparative benchmarking across multiple AI voice and LLM systems, identifying inconsistencies in biomedical and scientific terminology pronunciation. Contributed structured feedback aligned with human-in-the-loop (RLHF-style) methodologies to improve clarity, coherence, and domain-specific speech reliability. Accumulated 3,000+ hours of AI interaction and evaluation experience, including speech-text alignment and scientific terminology verificatio

I am involved I transcription validation, pronunciation accuracy assessment and grading of AI generated voice outputs. Executed comparative benchmarking across multiple AI voice and LLM systems, identifying inconsistencies in biomedical and scientific terminology pronunciation. Contributed structured feedback aligned with human-in-the-loop (RLHF-style) methodologies to improve clarity, coherence, and domain-specific speech reliability. Accumulated 3,000+ hours of AI interaction and evaluation experience, including speech-text alignment and scientific terminology verificatio

2021 - 2025

Education

Work History