Allan Kimani

Key Skills

Software

Img Lab

Labelbox

Roboflow

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Top Label Types

Bounding Box

Entity Ner Classification

Classification

Text Summarization

Evaluation Rating

Freelancer Overview

I am an experienced AI Trainer and Data Annotation Specialist with a strong background in computer science and hands-on expertise in training, evaluating, and refining AI-generated outputs. My experience includes prompt analysis, data labeling, and quality assurance for NLP and digital content, where I have consistently identified errors, bias, and inconsistencies to improve model reliability. I am skilled at providing clear, guideline-based feedback and documentation, and I am comfortable working with structured data formats such as JSON and YAML. My strong analytical skills, attention to detail, and ability to follow complex instructions ensure high-quality results for AI training and data annotation projects. I thrive in remote, task-based environments and am committed to supporting the development of accurate and ethical AI systems.

Labeling Experience

AI Data Labeling & Annotation Specialist

LabelboxTextBounding BoxEntity Ner Classification

Worked on multiple AI training projects, annotating and labeling large datasets including text, images, and audio. Applied classification, entity recognition (NER), bounding box, and summarization techniques to prepare high-quality training data for machine learning models. Reviewed AI-generated outputs to detect errors, inconsistencies, and bias, providing detailed feedback for iterative model improvement. Used tools like Labelbox to ensure accurate, consistent, and guideline-compliant labeling, contributing to the development of reliable and effective AI systems.

Worked on multiple AI training projects, annotating and labeling large datasets including text, images, and audio. Applied classification, entity recognition (NER), bounding box, and summarization techniques to prepare high-quality training data for machine learning models. Reviewed AI-generated outputs to detect errors, inconsistencies, and bias, providing detailed feedback for iterative model improvement. Used tools like Labelbox to ensure accurate, consistent, and guideline-compliant labeling, contributing to the development of reliable and effective AI systems.

2024

Education

Work History