Clollins Crysany

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Top Label Types

Bounding Box

Point Key Point

Segmentation

Classification

Tracking

Emotion Recognition

Freelancer Overview

I am an experienced AI data annotation and machine learning specialist with over 12 years in data operations and more than 9 years focused on developing high-quality training data for AI systems. My background spans large-scale dataset annotation, quality assurance, and workflow optimization, with hands-on experience supporting both natural language processing (NLP) and computer vision projects. I have led annotation teams at Microsoft and Amazon Web Services, overseeing quality control and collaborating closely with AI engineers and data scientists to improve model accuracy. My expertise includes implementing robust validation frameworks, mentoring junior specialists, and driving process improvements to increase efficiency and precision in data labeling. I am passionate about delivering reliable, accurate training datasets that empower cutting-edge AI solutions across diverse domains.

Labeling Experience

Enterprise LLM Training Data Annotation & Evaluation Project

LabelboxVideoBounding BoxPoint Key Point

Contributed to large-scale video annotation projects supporting computer vision and AI model development across multiple industries, including autonomous systems, security analytics, and activity recognition. Responsibilities included: Frame-by-frame video annotation for object detection and tracking Bounding box and polygon labeling of moving objects Action recognition tagging (e.g., walking, running, lifting, driving behaviors) Multi-object tracking across sequential frames Event detection and temporal segmentation Quality assurance review of annotated video datasets Worked with high-resolution video datasets exceeding 10,000+ annotated video clips, ensuring strict adherence to detailed labeling guidelines and frame-level precision requirements. Maintained 97–99% annotation accuracy while meeting tight project deadlines. Performed secondary quality validation and error correction to ensure dataset consistency and model-readiness. Collaborated with ML engineers to refine lab

Contributed to large-scale video annotation projects supporting computer vision and AI model development across multiple industries, including autonomous systems, security analytics, and activity recognition. Responsibilities included: Frame-by-frame video annotation for object detection and tracking Bounding box and polygon labeling of moving objects Action recognition tagging (e.g., walking, running, lifting, driving behaviors) Multi-object tracking across sequential frames Event detection and temporal segmentation Quality assurance review of annotated video datasets Worked with high-resolution video datasets exceeding 10,000+ annotated video clips, ensuring strict adherence to detailed labeling guidelines and frame-level precision requirements. Maintained 97–99% annotation accuracy while meeting tight project deadlines. Performed secondary quality validation and error correction to ensure dataset consistency and model-readiness. Collaborated with ML engineers to refine lab

2024

Enterprise LLM Training Data Annotation & Evaluation Project

LabelboxImageBounding BoxPoint Key Point

Led large-scale text annotation and evaluation efforts supporting enterprise-grade Large Language Model (LLM) development. The project involved labeling, validating, and refining high-volume datasets used for supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). Key responsibilities included: Named Entity Recognition (NER) tagging across diverse domains Prompt–response evaluation and quality rating Bias detection and content moderation review Text classification and summarization validation Writing high-quality instruction–response pairs for model fine-tuning Error analysis and dataset refinement The project covered over 500,000+ text samples across multiple subject domains. Maintained 98%+ annotation accuracy while adhering strictly to detailed client guidelines and compliance standards. Collaborated closely with AI engineers and data scientists to improve dataset structure, reduce ambiguity, and enhance model performance outcomes.

Led large-scale text annotation and evaluation efforts supporting enterprise-grade Large Language Model (LLM) development. The project involved labeling, validating, and refining high-volume datasets used for supervised fine-tuning (SFT) and reinforcement learning from human feedback (RLHF). Key responsibilities included: Named Entity Recognition (NER) tagging across diverse domains Prompt–response evaluation and quality rating Bias detection and content moderation review Text classification and summarization validation Writing high-quality instruction–response pairs for model fine-tuning Error analysis and dataset refinement The project covered over 500,000+ text samples across multiple subject domains. Maintained 98%+ annotation accuracy while adhering strictly to detailed client guidelines and compliance standards. Collaborated closely with AI engineers and data scientists to improve dataset structure, reduce ambiguity, and enhance model performance outcomes.

2022 - 2024

Education

Work History