Daniel Arinze

Key Skills

Software

Appen

CVAT

Labelbox

Toloka

Top Subject Matter

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Top Data Types

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Text

Top Label Types

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Evaluation Rating

Prompt Response Writing SFT

Question Answering

RLHF

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Freelancer Overview

I am a detail-oriented Computer Science graduate with hands-on experience in AI data annotation, content evaluation, and large language model assessment. My background includes evaluating AI-generated text for coherence, factual accuracy, and policy compliance, as well as performing labeling tasks for NLP datasets such as sentiment, intent, and topic classification. I am skilled in identifying bias, hallucinations, and inconsistencies in model outputs, and have developed structured evaluation reports to improve model performance. Proficient with Python, JavaScript, and tools like ChatGPT, Google Workspace, and data entry platforms, I consistently deliver high-quality results while following strict guidelines. My strong analytical skills, attention to detail, and experience working remotely with international teams make me well-equipped to support AI training data projects.

Labeling Experience

AI Model Evaluation & NLP Data Annotation Specialist

CVATTextEntity Ner ClassificationText Summarization

Worked on large-scale AI training and evaluation projects focused on improving Large Language Model (LLM) performance, safety, and response quality. Project Scope: Annotated and reviewed 1,000+ text samples. Contributed to dataset refinement for fine-tuning conversational AI systems Worked independently in a remote environment with structured QA feedback loops Quality Measures: Adhered strictly to project annotation rubrics Cross-checked responses using structured evaluation framework Maintained high inter-annotator agreement standards

Worked on large-scale AI training and evaluation projects focused on improving Large Language Model (LLM) performance, safety, and response quality. Project Scope: Annotated and reviewed 1,000+ text samples. Contributed to dataset refinement for fine-tuning conversational AI systems Worked independently in a remote environment with structured QA feedback loops Quality Measures: Adhered strictly to project annotation rubrics Cross-checked responses using structured evaluation framework Maintained high inter-annotator agreement standards

2024

Education

Work History