Ejikeye Olusegun

Key Skills

Software

Appen

Clickworker

CrowdSource

Data Annotation Tech

SuperAnnotate

Surge AI

Toloka

Telus

Other

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Document

Image

Text

Video

Top Label Types

Action Recognition

Audio Recording

Classification

Data Collection

Evaluation Rating

Prompt Response Writing SFT

Text Generation

Text Summarization

Transcription

Translation Localization

Freelancer Overview

I am an AI content specialist and linguistic analyst with hands-on experience in data labeling, multimedia annotation, and AI training data creation. My work involves analyzing and describing visual and digital content for large-scale AI datasets, ensuring semantic accuracy, cultural relevance, and adherence to detailed annotation guidelines. I excel at identifying subtle context, tone, and intent in images, videos, and text, and consistently deliver high-quality, structured data for machine learning applications. My background spans content quality evaluation, linguistic review, and cross-cultural communication, with a strong focus on internet culture and social media trends. I am fluent in both English and Yoruba, and I have contributed to projects involving computer vision, NLP, and localization for diverse global audiences.

Labeling Experience

AI Trainer

CrowdsourceImageEvaluation Rating

In my previous AI data and linguistic annotation projects with RWS Group. I worked on large-scale language and multimedia datasets used to train and evaluate NLP and multimodal AI models. Scope of the project: The projects involved preparing and evaluating text, audio, and visual content for AI training. This included semantic analysis, tone and intent classification, multimedia description, and linguistic quality review to improve model understanding of context, meaning, and cultural nuance. Specific data labeling tasks performed: Annotating and categorizing text and multimedia content. Writing structured descriptions of visual and audio content. Labeling tone, sentiment, and speaker intent. Flagging inconsistencies and guideline violations. Project size: I contributed to high-volume datasets containing thousands of items across multiple content types. Quality measures adhered to: Strict compliance with detailed annotation and evaluation guidelines. Linguistic accuracy.

In my previous AI data and linguistic annotation projects with RWS Group. I worked on large-scale language and multimedia datasets used to train and evaluate NLP and multimodal AI models. Scope of the project: The projects involved preparing and evaluating text, audio, and visual content for AI training. This included semantic analysis, tone and intent classification, multimedia description, and linguistic quality review to improve model understanding of context, meaning, and cultural nuance. Specific data labeling tasks performed: Annotating and categorizing text and multimedia content. Writing structured descriptions of visual and audio content. Labeling tone, sentiment, and speaker intent. Flagging inconsistencies and guideline violations. Project size: I contributed to high-volume datasets containing thousands of items across multiple content types. Quality measures adhered to: Strict compliance with detailed annotation and evaluation guidelines. Linguistic accuracy.

2024

Education

Work History