Keith Kibet

Key Skills

Software

Mindrift

Top Subject Matter

Computer Vision

Human Attribute Detection

AI for Arts

Top Data Types

Video

Image

Text

Document

Top Label Types

Tracking

Classification

Freelancer Overview

Elite Video Annotation Specialist. Core strengths include Mindrift. Education includes Bachelor of Science, University of Nairobi (2024) and Professional Certificate, Coursera (2023). AI-training focus includes data types such as Video and Image and labeling workflows including Tracking, Classification, and Evaluation.

Labeling Experience

Image/Video Annotation Specialist

MindriftImage

As an Image/Video Annotation Specialist, I evaluated multimodal data for AI model development. This required precision annotation to ensure visual accuracy and data consistency across projects. I regularly interpreted evolving guidelines for quality benchmarking and protocol compliance. • Performed complex content evaluation on images and videos. • Assessed data accuracy to create high-quality computer vision training sets. • Maintained 100% compliance with project-specific quality and productivity targets. • Ensured data reliability through adaptation to evolving annotation instructions.

As an Image/Video Annotation Specialist, I evaluated multimodal data for AI model development. This required precision annotation to ensure visual accuracy and data consistency across projects. I regularly interpreted evolving guidelines for quality benchmarking and protocol compliance. • Performed complex content evaluation on images and videos. • Assessed data accuracy to create high-quality computer vision training sets. • Maintained 100% compliance with project-specific quality and productivity targets. • Ensured data reliability through adaptation to evolving annotation instructions.

2025 - 2025

Elite Video Annotation Specialist

MindriftVideoTracking

As an Elite Video Annotation Specialist, I labeled video data to support computer vision and human-attribute AI projects. I maintained a consistent high-quality standard, holding the Elite Badge and a Quality Rating of 4.5/5. My work involved collaborating with QA teams to refine annotation guidelines for challenging cases. • Performed complex temporal labeling to ensure continuity and accuracy across frames. • Specialized in autonomous and human-attribute model labeling tasks. • Maintained top-tier accuracy and consistency for elite status on the annotation platform. • Delivered high-fidelity tracking and annotation for critical AI model inputs.

As an Elite Video Annotation Specialist, I labeled video data to support computer vision and human-attribute AI projects. I maintained a consistent high-quality standard, holding the Elite Badge and a Quality Rating of 4.5/5. My work involved collaborating with QA teams to refine annotation guidelines for challenging cases. • Performed complex temporal labeling to ensure continuity and accuracy across frames. • Specialized in autonomous and human-attribute model labeling tasks. • Maintained top-tier accuracy and consistency for elite status on the annotation platform. • Delivered high-fidelity tracking and annotation for critical AI model inputs.

2024 - 2025

AI Image Review & Age Classification Specialist

MindriftImageClassification

In my role as an AI Image Review & Age Classification Specialist, I classified images by analyzing visual and demographic attributes. I ensured accurate age group categorization by examining facial features and contextual cues for AI model training. The work demanded strict adherence to guidelines and a focus on data reliability. • Reviewed human images and assigned to defined age classes. • Upheld high inter-annotator agreement and passed quality audits consistently. • Flagged ambiguous or low-quality data to enhance overall dataset quality. • Used detailed guidelines to improve computer vision model performance.

In my role as an AI Image Review & Age Classification Specialist, I classified images by analyzing visual and demographic attributes. I ensured accurate age group categorization by examining facial features and contextual cues for AI model training. The work demanded strict adherence to guidelines and a focus on data reliability. • Reviewed human images and assigned to defined age classes. • Upheld high inter-annotator agreement and passed quality audits consistently. • Flagged ambiguous or low-quality data to enhance overall dataset quality. • Used detailed guidelines to improve computer vision model performance.

2024 - 2024

Education

Work History