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Femi Akintayo

Femi Akintayo

AI Data Annotator

Nigeria flagLagos, Nigeria
$10.00/hrEntry LevelOther

Key Skills

Software

Other

Top Subject Matter

Image Captioning
Multimodal AI
Image Generation

Top Data Types

AudioAudio
ImageImage
VideoVideo
TextText

Top Task Types

Audio RecordingAudio Recording
ClassificationClassification
Object DetectionObject Detection
SegmentationSegmentation
Text GenerationText Generation
Translation/LocalizationTranslation/Localization
Action RecognitionAction Recognition
TranscriptionTranscription
Entity (NER) ClassificationEntity (NER) Classification

Freelancer Overview

I am an accuracy focused data specialist with experience in data labeling, specifically within the domain of Natural Language Processing (NLP) and Computer Vision. My expertise includes managing specific Artificial Intelligence tasks where I have performed semantic segmentation, named entity recognition (NER), and sentiment analysis to fine-tune ML models. I have an eye for detail and ability to comprehend complex guidelines and requirement thereby delivering results that are accurate and ethically sourced and also ensuring the resulting AI models are robust and unbiased.

Entry LevelYorubaEnglishEsperanto

Labeling Experience

AI Data Annotator – Content Understanding Entity Verification

OtherTextEntity Ner Classification
I verified and classified entities within text samples to enable structured extraction and confidence scoring for content accuracy. My role focused on semantic analysis and improving the precision of entity recognition for training NLP models. This supported downstream content understanding applications for AI systems. • Conducted entity verification and semantic analysis • Focused on accurate data extraction from text • Trained models on confidence scoring methodologies • Enabled better structured content evaluation for NLP

I verified and classified entities within text samples to enable structured extraction and confidence scoring for content accuracy. My role focused on semantic analysis and improving the precision of entity recognition for training NLP models. This supported downstream content understanding applications for AI systems. • Conducted entity verification and semantic analysis • Focused on accurate data extraction from text • Trained models on confidence scoring methodologies • Enabled better structured content evaluation for NLP

2025 - Present

AI Data Annotator – Audio Sentiment Transcription & Segmentation

OtherAudioTranscription
I segmented and transcribed user turn audio to capture sentiment in conversational data. The process involved accurate auditory labeling and text conversion, supporting audio sentiment analysis. This served in training models for understanding user sentiment in speech. • Worked on Gen AI Audio Sentiment projects • Performed user turn segmentation and transcription • Contributed to sentiment analysis dataset creation • Enhanced audio AI models for nuanced understanding

I segmented and transcribed user turn audio to capture sentiment in conversational data. The process involved accurate auditory labeling and text conversion, supporting audio sentiment analysis. This served in training models for understanding user sentiment in speech. • Worked on Gen AI Audio Sentiment projects • Performed user turn segmentation and transcription • Contributed to sentiment analysis dataset creation • Enhanced audio AI models for nuanced understanding

2025 - Present

AI Data Annotator – Gen AI Text Generation Hallucination Study

OtherTextText Generation
I evaluated AI-generated text outputs against prompts to identify hallucinated or inaccurate content. This labeling ensured generated text met fidelity requirements and avoided erroneous model responses. My efforts contributed to strengthening generative AI reliability. • Conducted hallucination studies for Gen AI Text Generation • Compared prompt input to machine-generated output • Identified and flagged instances of hallucination • Provided feedback to enhance model output accuracy

I evaluated AI-generated text outputs against prompts to identify hallucinated or inaccurate content. This labeling ensured generated text met fidelity requirements and avoided erroneous model responses. My efforts contributed to strengthening generative AI reliability. • Conducted hallucination studies for Gen AI Text Generation • Compared prompt input to machine-generated output • Identified and flagged instances of hallucination • Provided feedback to enhance model output accuracy

2025 - Present

AI Data Annotator – Human Egocentric Video Annotation

OtherVideoAction Recognition
I labeled egocentric video data captured from wearable cameras, annotating actions and interactions as seen from the subject's perspective. My annotation work enabled models to interpret first-person activity and attention. This contributed to advancements in understanding human behavior from video footage. • Dextera Ego project involvement • Annotated first-person activities and interactions • Supported action and attention recognition in video datasets • Improved AI capability in egocentric video analysis

I labeled egocentric video data captured from wearable cameras, annotating actions and interactions as seen from the subject's perspective. My annotation work enabled models to interpret first-person activity and attention. This contributed to advancements in understanding human behavior from video footage. • Dextera Ego project involvement • Annotated first-person activities and interactions • Supported action and attention recognition in video datasets • Improved AI capability in egocentric video analysis

2025 - Present

AI Data Annotator – Standalone Image Error Tagging

OtherImage
I performed error analysis on AI-generated images, identifying and tagging issues such as misplacement, blurriness, and unrealistic elements. My task included perusing standalone images to recognize and document visual errors relevant to model training. This process improved the quality of automated image generation. • Worked with Gen AI Standalone Image Error Tagging • Detected a variety of image quality errors and anomalies • Collaborated on refining generative AI models • Enhanced model robustness via thorough error documentation

I performed error analysis on AI-generated images, identifying and tagging issues such as misplacement, blurriness, and unrealistic elements. My task included perusing standalone images to recognize and document visual errors relevant to model training. This process improved the quality of automated image generation. • Worked with Gen AI Standalone Image Error Tagging • Detected a variety of image quality errors and anomalies • Collaborated on refining generative AI models • Enhanced model robustness via thorough error documentation

2025 - Present

Education

O

Olabisi Onabanjo University

Postgraduate Diploma, Finance

Postgraduate Diploma
2019 - 2019
O

Olabisi Onabanjo University

Bachelor of Science, Banking and Finance

Bachelor of Science
2014 - 2014

Work History

O

Otaku.Hugo Company Ltd

AI Data Annotator

Lagos
2025 - Present