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Gold Komolafe

Gold Komolafe

AI Content Evaluation & Annotation Specialist

Nigeria flagN/A, Nigeria
IntermediateLabel Studio

Key Skills

Software

Label StudioLabel Studio

Top Subject Matter

Text AI output evaluation
Sentiment Domain Expertise
Intent Domain Expertise

Top Data Types

TextText
ImageImage
DocumentDocument

Top Task Types

No task types listed

Freelancer Overview

AI Content Evaluation & Annotation Specialist. Brings 1+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Label Studio. Education includes Bachelor of Technology, Federal University of Technology, Akure (2028). AI-training focus includes data types such as Text and labeling workflows including Evaluation and Rating.

Intermediate

Labeling Experience

Label Studio

AI Content Evaluation & Annotation Specialist

Label StudioText
As an AI Content Evaluation & Annotation Specialist, I evaluated and annotated a wide variety of AI-generated text data. My tasks included applying structured rubrics, scoring model responses, and iteratively refining the data labeling according to feedback cycles. I contributed to the uniformity and accuracy of training datasets in high-volume, time-sensitive production environments. • Applied annotation schemas for sentiment, intent, and factual correctness to support model fine-tuning. • Conducted A/B preference ranking and RLHF feedback for large language models. • Identified edge cases, hallucinations, and policy violations, documenting with detailed justifications. • Used industry-standard tools and maintained high annotation throughput with minimal rework.

As an AI Content Evaluation & Annotation Specialist, I evaluated and annotated a wide variety of AI-generated text data. My tasks included applying structured rubrics, scoring model responses, and iteratively refining the data labeling according to feedback cycles. I contributed to the uniformity and accuracy of training datasets in high-volume, time-sensitive production environments. • Applied annotation schemas for sentiment, intent, and factual correctness to support model fine-tuning. • Conducted A/B preference ranking and RLHF feedback for large language models. • Identified edge cases, hallucinations, and policy violations, documenting with detailed justifications. • Used industry-standard tools and maintained high annotation throughput with minimal rework.

2023 - Present

Education

F

Federal University of Technology, Akure

Bachelor of Technology, Project Management Technology

Bachelor of Technology
2023 - 2028

Work History

N

Nigeria LNG Limited

Mechanical Craftsman Intern

N/A
2022 - 2022