Boluwatife Edun

Key Skills

Software

Label Studio

Doccano

CVAT

Top Subject Matter

AI Training Specialist

Data Annotation Specialist

Data Quality Reviewer

Top Data Types

Text

Image

Document

Top Task Types

RLHF

Entity (NER) Classification

Bounding Box

Freelancer Overview

AI Training Specialist (Freelance/Contract). Brings 5+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include Label Studio, Doccano, and CVAT. Education includes Bachelor of Science, University of Greenwich (2022). AI-training focus includes data types such as Text and Image and labeling workflows including RLHF, Entity (NER) Classification, and Bounding Box.

Labeling Experience

AI Training Specialist (Freelance/Contract)

Label StudioTextRLHF

As an AI Training Specialist, I evaluated thousands of AI-generated responses for accuracy, bias, and adherence to system instructions. I authored high-quality Golden Responses to serve as ground-truth data for LLM fine-tuning. Deep-dive fact-checking was performed on technical topics such as financial market analysis and macroeconomics, ensuring top accuracy. • Reviewed and rated LLM outputs with meticulous attention to detail. • Crafted prompt-based instruction-response examples and evaluation sets. • Focused on guideline interpretation, feedback quality, and bias minimization. • Used iterative feedback to improve data quality and guideline coverage.

As an AI Training Specialist, I evaluated thousands of AI-generated responses for accuracy, bias, and adherence to system instructions. I authored high-quality Golden Responses to serve as ground-truth data for LLM fine-tuning. Deep-dive fact-checking was performed on technical topics such as financial market analysis and macroeconomics, ensuring top accuracy. • Reviewed and rated LLM outputs with meticulous attention to detail. • Crafted prompt-based instruction-response examples and evaluation sets. • Focused on guideline interpretation, feedback quality, and bias minimization. • Used iterative feedback to improve data quality and guideline coverage.

2023 - Present

Data Annotation Specialist (Independent Contractor)

DoccanoTextEntity Ner Classification

As a Data Annotation Specialist, I delivered annotations for NLP and LLM tasks including intent classification, sentiment, emotion labelling, and NER. Key contributions included scoring instruction-response datasets, summarization evaluation, safety policy labelling, and detailed QA of completed annotation batches. I maintained edge-case logs and clarified labelling guidelines to ensure consistence and minimize errors. • Performed dataset readiness checks and validated file schema/metadata. • Conducted QA spot checks, label distribution and contradiction scans. • Collaborated to resolve ambiguous cases and streamline rules. • Ensured high-quality, well-documented data for LLM development.

As a Data Annotation Specialist, I delivered annotations for NLP and LLM tasks including intent classification, sentiment, emotion labelling, and NER. Key contributions included scoring instruction-response datasets, summarization evaluation, safety policy labelling, and detailed QA of completed annotation batches. I maintained edge-case logs and clarified labelling guidelines to ensure consistence and minimize errors. • Performed dataset readiness checks and validated file schema/metadata. • Conducted QA spot checks, label distribution and contradiction scans. • Collaborated to resolve ambiguous cases and streamline rules. • Ensured high-quality, well-documented data for LLM development.

2022 - Present

Computer Vision Annotator (Image & Video)

CVATImageBounding Box

As a Computer Vision Annotator, I labelled image and video datasets using bounding boxes, polygons, masks, and keypoints to support CV model training. I addressed occlusions, small objects, and motion blur while ensuring consistent application of guidelines. Systematic QC and rework cycles improved batch acceptance rates and reduced downstream corrections. • Confirmed boundary rules, class definitions, and hierarchy standards. • Used feedback logs to align corrections across samples. • Focused on tight bounding, accurate class selection, and attribute consistency. • Delivered high-throughput, clean labels under strict deadlines.

As a Computer Vision Annotator, I labelled image and video datasets using bounding boxes, polygons, masks, and keypoints to support CV model training. I addressed occlusions, small objects, and motion blur while ensuring consistent application of guidelines. Systematic QC and rework cycles improved batch acceptance rates and reduced downstream corrections. • Confirmed boundary rules, class definitions, and hierarchy standards. • Used feedback logs to align corrections across samples. • Focused on tight bounding, accurate class selection, and attribute consistency. • Delivered high-throughput, clean labels under strict deadlines.

2023 - 2024

Data Quality Reviewer (Annotation QA)

Label StudioText

Working as a Data Quality Reviewer, I conducted second-pass QA on labelled datasets, checking for class confusion, missed entities, boundary errors, and guideline compliance. I performed sample-based audits to verify that annotated data met quality thresholds. Feedback notes and error logs were produced to calibrate annotator accuracy and reduce disagreement. • Ensured dataset acceptance rates through rigorous QA methods. • Highlighted error patterns and advised annotators with examples. • Maintained escalation and error pattern logs for team learning. • Monitored inter-annotator agreement and batch compliance systematically.

Working as a Data Quality Reviewer, I conducted second-pass QA on labelled datasets, checking for class confusion, missed entities, boundary errors, and guideline compliance. I performed sample-based audits to verify that annotated data met quality thresholds. Feedback notes and error logs were produced to calibrate annotator accuracy and reduce disagreement. • Ensured dataset acceptance rates through rigorous QA methods. • Highlighted error patterns and advised annotators with examples. • Maintained escalation and error pattern logs for team learning. • Monitored inter-annotator agreement and batch compliance systematically.

2022 - 2023

Education

Work History