Victor Udoye

Key Skills

Software

OneForma

LabelImg

Mercor

Top Subject Matter

Large Action Model Training

Web3 Domain Expertise

Blockchain Domain Expertise

Top Data Types

Text

Audio

Document

Top Task Types

Classification

Transcription

Freelancer Overview

Data Annotation Specialist. Brings 10+ years of professional experience across legal operations, contract review, compliance, and structured analysis. Core strengths include ActionModel, OneForma, and Upwork. Education includes Bachelor of Engineering, Federal University of Technology, Owerri (2021). AI-training focus includes data types such as Text and Audio and labeling workflows including Classification, Transcription, and Evaluation.

Labeling Experience

Data Annotation Specialist

TextClassification

I managed annotation workflows for training Large Action Models (LAMs), focusing on AI agent supervision for improving action recognition in web3 and blockchain contexts. My work centered on classifying text data for sentiment analysis and intent recognition, ensuring compliance with evolving client specifications. Quality assurance activities were performed to maintain high data integrity and project alignment. • Coordinated comprehensive annotation and review processes for LAM training datasets. • Applied advanced taxonomies to improve the accuracy of AI behavioral modeling. • Conducted regular QA to meet updated benchmarks for LAM performance. • Delivered effective feedback to engineering for continuous model improvement.

I managed annotation workflows for training Large Action Models (LAMs), focusing on AI agent supervision for improving action recognition in web3 and blockchain contexts. My work centered on classifying text data for sentiment analysis and intent recognition, ensuring compliance with evolving client specifications. Quality assurance activities were performed to maintain high data integrity and project alignment. • Coordinated comprehensive annotation and review processes for LAM training datasets. • Applied advanced taxonomies to improve the accuracy of AI behavioral modeling. • Conducted regular QA to meet updated benchmarks for LAM performance. • Delivered effective feedback to engineering for continuous model improvement.

2025 - Present

AI Data Evaluator (Contract)

Audio

I compared Large Language Model (LLM) transcriptions to audio sources, evaluating semantic accuracy, speaker diarization, and contextual retention. My annotations adhered strictly to taxonomies and contributed actionable feedback, achieving a high-quality standard. Collaboration with project leaders led to refined evaluation guidelines and reduced model error rates. • Conducted semantic and contextual evaluation of LLM-generated transcriptions. • Achieved a 98% QA score through stringent guideline adherence. • Provided actionable quality improvement feedback to clients. • Influenced project-wide reduction in model errors with ongoing feedback.

I compared Large Language Model (LLM) transcriptions to audio sources, evaluating semantic accuracy, speaker diarization, and contextual retention. My annotations adhered strictly to taxonomies and contributed actionable feedback, achieving a high-quality standard. Collaboration with project leaders led to refined evaluation guidelines and reduced model error rates. • Conducted semantic and contextual evaluation of LLM-generated transcriptions. • Achieved a 98% QA score through stringent guideline adherence. • Provided actionable quality improvement feedback to clients. • Influenced project-wide reduction in model errors with ongoing feedback.

2025 - 2025

Data Annotation Specialist / Quality Assurance Analyst (Contract)

OneformaAudioTranscription

I managed annotation workflows for audio data used in AI training, ensuring high-quality transcription and accurate formatting for global AI model clients. Classification tasks were performed with complex taxonomies for sentiment and intent assessment. My comparative A/B testing and feedback helped optimize conversational AI model outputs. • Oversaw annotation and QA for audio-based training data on contracted projects. • Executed transcription accuracy checks for global deployment. • Utilized taxonomies to classify and enhance sentiment and intent datasets. • Authored detailed feedback reports identifying model failure points.

I managed annotation workflows for audio data used in AI training, ensuring high-quality transcription and accurate formatting for global AI model clients. Classification tasks were performed with complex taxonomies for sentiment and intent assessment. My comparative A/B testing and feedback helped optimize conversational AI model outputs. • Oversaw annotation and QA for audio-based training data on contracted projects. • Executed transcription accuracy checks for global deployment. • Utilized taxonomies to classify and enhance sentiment and intent datasets. • Authored detailed feedback reports identifying model failure points.

2025 - 2025

LLM Audio Transcription Evaluation | Contract Project

Audio

I participated in a contract project evaluating generative AI’s ability to transcribe accented English from conference call audio. My responsibility included rating the accuracy of transcriptions using pairwise ranking and Likert scale assessments. Weekly, I contributed over fifty-six qualitative justifications to inform rapid AI model improvements. • Evaluated AI transcription accuracy with specialized rating techniques. • Focused on accented English and conference call scenarios. • Documented findings and justifications for engineering use. • Helped drive measurable improvements in AI transcription quality.

I participated in a contract project evaluating generative AI’s ability to transcribe accented English from conference call audio. My responsibility included rating the accuracy of transcriptions using pairwise ranking and Likert scale assessments. Weekly, I contributed over fifty-six qualitative justifications to inform rapid AI model improvements. • Evaluated AI transcription accuracy with specialized rating techniques. • Focused on accented English and conference call scenarios. • Documented findings and justifications for engineering use. • Helped drive measurable improvements in AI transcription quality.

Not specified

Education

Work History