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Ekanem Kingsley

Ekanem Kingsley

AI Trainer & Data Annotator | NLP, Image Annotation, Data Labeling & QA

Nigeria flagLagos, Nigeria
$10.00/hrExpertCVATLabelboxDon T Disclose

Key Skills

Software

CVATCVAT
LabelboxLabelbox
Don't disclose

Top Subject Matter

IT & Information

Top Data Types

ImageImage
VideoVideo
TextText

Top Task Types

Bounding BoxBounding Box
ClassificationClassification
SegmentationSegmentation
Text GenerationText Generation
Object DetectionObject Detection
TranscriptionTranscription
Evaluation/RatingEvaluation/Rating
Data CollectionData Collection

Freelancer Overview

Expert Data Annotation & AI Training specialist with years of experience. I deliver high-quality labeled datasets for text, image, and NLP tasks. Skilled in QA, validation, and scalable workflows. Reliable, detail-oriented, and deadline-driven. Open to remote collaboration.

ExpertEnglishYoruba

Labeling Experience

Video Annotation

VideoClassification
At Hugo, I worked on video classification for sensitive and non-sensitive media as well as describing video contents. The scope involved reviewing video content and assigning classification labels based on predefined sensitivity criteria. Tasks included classifying frames or entire clips and writing brief descriptions of video content. The project size covered hundreds of video clips. Quality measures followed strict sensitivity guidelines, periodic consensus checks, and random audits to ensure accurate, consistent labeling. At Atlas Capture, I worked on video annotation, including classification and segmentation. The scope involved labeling sensitive/non-sensitive media, describing content, and performing frame-level video segmentation. Tasks included tagging object boundaries and actions across clips. Project size covered hundreds of videos. Quality measures followed strict guidelines, inter-annotator checks, and random audits to ensure precision and consistency.

At Hugo, I worked on video classification for sensitive and non-sensitive media as well as describing video contents. The scope involved reviewing video content and assigning classification labels based on predefined sensitivity criteria. Tasks included classifying frames or entire clips and writing brief descriptions of video content. The project size covered hundreds of video clips. Quality measures followed strict sensitivity guidelines, periodic consensus checks, and random audits to ensure accurate, consistent labeling. At Atlas Capture, I worked on video annotation, including classification and segmentation. The scope involved labeling sensitive/non-sensitive media, describing content, and performing frame-level video segmentation. Tasks included tagging object boundaries and actions across clips. Project size covered hundreds of videos. Quality measures followed strict guidelines, inter-annotator checks, and random audits to ensure precision and consistency.

2023 - Present

Model Evaluation

TextEvaluation Rating
At HUGO, I worked on Multimodal Language Modelling, Generated Context evaluation, Model Personas, and External Search. The scope involved assessing generated text outputs for relevance, coherence, and alignment with model personas. Tasks included rating context quality, fact-checking external search results, and scoring response accuracy across diverse prompts. The project size covered hundreds of model outputs per cycle. Quality measures included rubric-based scoring, blind re-evaluation, and calibration sessions to ensure consistent, objective ratings.

At HUGO, I worked on Multimodal Language Modelling, Generated Context evaluation, Model Personas, and External Search. The scope involved assessing generated text outputs for relevance, coherence, and alignment with model personas. Tasks included rating context quality, fact-checking external search results, and scoring response accuracy across diverse prompts. The project size covered hundreds of model outputs per cycle. Quality measures included rubric-based scoring, blind re-evaluation, and calibration sessions to ensure consistent, objective ratings.

2023 - Present

Descriptive/ Creative writing

ImageText Generation
At Hawkeye, I worked on image description projects where the scope was to capture every visible detail—objects, spatial relationships, colors, actions, and context. Tasks included dense captioning and attribute labeling. Project sizes ranged from hundreds to thousands of images. Quality measures included guideline adherence, inter-annotator consistency checks, and regular validation rounds to ensure precision and completeness.

At Hawkeye, I worked on image description projects where the scope was to capture every visible detail—objects, spatial relationships, colors, actions, and context. Tasks included dense captioning and attribute labeling. Project sizes ranged from hundreds to thousands of images. Quality measures included guideline adherence, inter-annotator consistency checks, and regular validation rounds to ensure precision and completeness.

2026 - 2026

3D Modelling

3D SensorSegmentation
At 3dfy & One Vision for HUGO, I worked on 3D sensor data segmentation. The scope involved labeling 3D scans for accurate object isolation. Tasks included pixel-perfect segmentation of garments and accessories across complex surfaces. Project size covered hundreds of 3D frames. Quality measures followed strict annotation guidelines, inter-annotator agreement, and multi-pass validation for consistency.

At 3dfy & One Vision for HUGO, I worked on 3D sensor data segmentation. The scope involved labeling 3D scans for accurate object isolation. Tasks included pixel-perfect segmentation of garments and accessories across complex surfaces. Project size covered hundreds of 3D frames. Quality measures followed strict annotation guidelines, inter-annotator agreement, and multi-pass validation for consistency.

2025 - 2025

Audio Trascription

AudioTranscription
At HUGO, I worked on audio transcription for sentiment analysis. The scope involved converting spoken audio into accurate text while capturing emotional cues and tone shifts. Tasks included verbatim transcription and labeling sentiment indicators. Project size covered hundreds of audio clips. Quality measures included style guide adherence, cross-checking transcriptions, and regular feedback reviews to ensure consistency and high accuracy.

At HUGO, I worked on audio transcription for sentiment analysis. The scope involved converting spoken audio into accurate text while capturing emotional cues and tone shifts. Tasks included verbatim transcription and labeling sentiment indicators. Project size covered hundreds of audio clips. Quality measures included style guide adherence, cross-checking transcriptions, and regular feedback reviews to ensure consistency and high accuracy.

2024 - 2025

Education

A

AkwaIbom State University

B.Engr, Agricultural Engineering

B.Engr
2014 - 2020

Work History

R

Righteous Academy

Teacher

Iyanaipaja, Lagos state
2020 - 2026