Alfred Izunobi

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Task Types

Evaluation Rating

Prompt Response Writing SFT

Segmentation

Freelancer Overview

I have hands-on experience in data labeling and AI training through projects with Appen and Multimango, where I contributed to building high-quality datasets for computer vision, natural language processing, and machine learning tasks. My work involved accurately labeling images, text, and audio data following detailed project guidelines to ensure model accuracy and consistency. I’m skilled in using various annotation platforms and maintaining a high level of attention to detail while meeting strict quality and deadline standards. What sets me apart is my ability to quickly adapt to new labeling tools and project requirements, as well as my understanding of how labeled data impacts AI model performance and bias reduction. I bring a combination of precision, speed, and a strong grasp of data quality standards that help improve the effectiveness of AI systems across different domains.

Labeling Experience

Ogden - Evaluate the relevance of social media ads

AppenImageEvaluation Rating

As a data annotator for the Ogden Evaluation and Rating project, I was responsible for evaluating and rating AI-generated outputs across different domains such as search relevance, text quality, and conversational accuracy. The task involved reviewing system responses against detailed guidelines to assess accuracy, clarity, context relevance, tone, and overall quality. I ensured consistency and fairness in my evaluations by following predefined scoring rubrics and providing written feedback when necessary. This work directly contributed to improving AI model understanding, ranking algorithms, and natural language responses. Through this project, I developed strong analytical, linguistic, and judgment skills while maintaining high productivity and precision under tight deadlines.

As a data annotator for the Ogden Evaluation and Rating project, I was responsible for evaluating and rating AI-generated outputs across different domains such as search relevance, text quality, and conversational accuracy. The task involved reviewing system responses against detailed guidelines to assess accuracy, clarity, context relevance, tone, and overall quality. I ensured consistency and fairness in my evaluations by following predefined scoring rubrics and providing written feedback when necessary. This work directly contributed to improving AI model understanding, ranking algorithms, and natural language responses. Through this project, I developed strong analytical, linguistic, and judgment skills while maintaining high productivity and precision under tight deadlines.

2025

Education

Work History