Agbata Peter

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Top Label Types

Computer Programming Coding

Freelancer Overview

I am a Computer Science graduate with a strong foundation in data organization, analysis, and structured documentation, skills that directly support AI training and data labeling roles. My academic background and internship experience in web development and IT support involved system testing, identifying errors, organizing structured information, and documenting technical processes. I have consistently worked with maintaining accurate records, preparing reports, and ensuring data integrity, which aligns closely with AI data annotation and response evaluation tasks. I bring strong attention to detail, the ability to follow complex guidelines, and the discipline to work independently in remote environments while meeting deadlines. I am proficient in Microsoft Excel for data cleaning, sorting, and basic analysis, and I have excellent written communication skills for reviewing and refining content. My technical understanding of software systems and web technologies allows me to quickly grasp AI workflows, prompt structures, and quality standards. These combined skills position me to effectively contribute to AI training, data labeling, and model evaluation projects with accuracy and consistency.

Labeling Experience

computer programming AI trainer experience

AppenComputer Code ProgrammingComputer Programming Coding

the project focused on building large language models with exceptional programming and software engineering capabilities. The system is continuously trained on high quality, permissively-licensed code repositories, competitive programming problems, real world debugging traces, code review comments, API documentation, and synthetic multi-step coding exercises. The goal is to create models that write clean, efficient, well documented code across many languages, understand complex system architecture, debug realistically, and follow modern best practices approaching or surpassing mid to senior developer performance on practical software development tasks.

the project focused on building large language models with exceptional programming and software engineering capabilities. The system is continuously trained on high quality, permissively-licensed code repositories, competitive programming problems, real world debugging traces, code review comments, API documentation, and synthetic multi-step coding exercises. The goal is to create models that write clean, efficient, well documented code across many languages, understand complex system architecture, debug realistically, and follow modern best practices approaching or surpassing mid to senior developer performance on practical software development tasks.

2025 - 2025

Education

Work History