Raynauld Ronoh

Key Skills

Software

Labelbox

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Entity Ner Classification

Classification

Evaluation Rating

Transcription

Freelancer Overview

I have extensive experience designing and developing enterprise systems where high-quality data integrity and process automation were essential, particularly in payroll, HR, and supply chain domains. My background includes integrating complex data sources, optimizing workflows, and building secure, scalable platforms that handle sensitive information—skills directly transferable to data labeling and AI training data roles. I am proficient in Python, JavaScript, SQL, and cloud technologies such as AWS and Kubernetes, and have led teams in implementing API-driven data exchanges and real-time analytics dashboards. My work has consistently focused on ensuring accurate data processing, compliance, and automation, which I am eager to apply to projects involving data annotation, labeling, and AI model training.

Labeling Experience

Multimodal AI Dataset Annotation for LLM Training

LabelboxImageEntity Ner ClassificationClassification

I contributed to a large-scale AI training project for conversational and multimodal AI models, annotating 50,000+ text, audio, and image samples. Tasks included named entity recognition (NER), classification, transcription, QA evaluation, and model output rating. I ensured high-quality annotations by adhering to strict project guidelines, consistency checks, and peer review protocols, resulting in highly reliable datasets that improved model understanding and response accuracy. The project involved collaboration with data science and machine learning teams to optimize dataset structure, labeling efficiency, and AI performance evaluation.

I contributed to a large-scale AI training project for conversational and multimodal AI models, annotating 50,000+ text, audio, and image samples. Tasks included named entity recognition (NER), classification, transcription, QA evaluation, and model output rating. I ensured high-quality annotations by adhering to strict project guidelines, consistency checks, and peer review protocols, resulting in highly reliable datasets that improved model understanding and response accuracy. The project involved collaboration with data science and machine learning teams to optimize dataset structure, labeling efficiency, and AI performance evaluation.

2024 - 2025

Education

Work History