Ijadunola Muiz

Key Skills

Software

CVAT

Labelbox

AWS SageMaker

Top Subject Matter

Grocery/Product Recognition

Computer Vision

Inventory Management

Top Data Types

Image

Text

Document

Top Task Types

Bounding Box

Classification

Entity (NER) Classification

Freelancer Overview

AI Data Annotation Lead. Brings 6+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include CVAT, Labelbox, and AWS SageMaker. Education includes Bachelor of Engineering, Federal University of Technology Akure. AI-training focus includes data types such as Image and Text and labeling workflows including Bounding Box, Classification, and Entity (NER) Classification.

Labeling Experience

Data Processing & Frontend Annotator

ImageClassification

As a Data Processing & Frontend Annotator at Slobodan, I handled preprocessing and annotation of image datasets for computer vision pipelines. My responsibilities encompassed cleaning, resizing, and normalizing over 10,000 image assets, as well as annotating metadata for SEO and accessibility improvements. I also created data validation scripts to streamline ML workflows and minimize data corruption. • Structured image metadata and alt-text for better search relevance. • Reduced corrupt image uploads by 90% through pipeline optimization. • Ensured consistent input standards for training data. • Enhanced annotation efficiency for downstream ML tasks.

As a Data Processing & Frontend Annotator at Slobodan, I handled preprocessing and annotation of image datasets for computer vision pipelines. My responsibilities encompassed cleaning, resizing, and normalizing over 10,000 image assets, as well as annotating metadata for SEO and accessibility improvements. I also created data validation scripts to streamline ML workflows and minimize data corruption. • Structured image metadata and alt-text for better search relevance. • Reduced corrupt image uploads by 90% through pipeline optimization. • Ensured consistent input standards for training data. • Enhanced annotation efficiency for downstream ML tasks.

2024 - Present

Freelance Data Labeler (NLP Text Classification)

TextEntity Ner Classification

As a Freelance Data Labeler, I performed data labeling for NLP text classification projects involving sentiment analysis and named entity recognition tasks. I labeled and tagged over 8,000 customer support transcripts and product descriptions with sentiment categories and extracted key entities. My annotations contributed to training chatbot and search indexing algorithms with a 92% inter-annotator agreement rate. • Labeled customer support texts for sentiment analysis. • Extracted entities such as brands and quantities for enhanced search algorithms. • Achieved high reliability in annotation agreement metrics. • Contributed to chatbot and NLP model development through accurate labeling.

As a Freelance Data Labeler, I performed data labeling for NLP text classification projects involving sentiment analysis and named entity recognition tasks. I labeled and tagged over 8,000 customer support transcripts and product descriptions with sentiment categories and extracted key entities. My annotations contributed to training chatbot and search indexing algorithms with a 92% inter-annotator agreement rate. • Labeled customer support texts for sentiment analysis. • Extracted entities such as brands and quantities for enhanced search algorithms. • Achieved high reliability in annotation agreement metrics. • Contributed to chatbot and NLP model development through accurate labeling.

2023 - Present

AI Data Annotation Lead

CVATImageBounding Box

As the AI Data Annotation Lead at Fooddy, I led comprehensive image annotation efforts for a grocery recognition AI system. I managed and processed over 15,000 product images using bounding boxes, polygons, and semantic segmentation, while implementing rigorous quality assurance protocols. These initiatives directly contributed to inventory management models and improved the accuracy of machine learning outputs. • Administered projects using CVAT and Labelbox, configuring workflows in multiple export formats. • Authored labeling guidelines, reducing onboarding and error rates for annotators. • Performed inter-annotator agreement reviews and reduced inconsistencies by 40%. • Collaborated with ML engineers, iteratively updating datasets based on model feedback.

As the AI Data Annotation Lead at Fooddy, I led comprehensive image annotation efforts for a grocery recognition AI system. I managed and processed over 15,000 product images using bounding boxes, polygons, and semantic segmentation, while implementing rigorous quality assurance protocols. These initiatives directly contributed to inventory management models and improved the accuracy of machine learning outputs. • Administered projects using CVAT and Labelbox, configuring workflows in multiple export formats. • Authored labeling guidelines, reducing onboarding and error rates for annotators. • Performed inter-annotator agreement reviews and reduced inconsistencies by 40%. • Collaborated with ML engineers, iteratively updating datasets based on model feedback.

2021 - Present

Computer Vision Annotation (Fooddy AI Dataset)

CVATImageBounding Box

For the Fooddy AI Dataset project, I annotated over 20,000 grocery items with bounding boxes and class labels for mobile object detection models. I performed pixel-level semantic segmentation for shelf analysis and automated inventory audits with high accuracy. My work also included automated validation for class imbalance and edge cases to strengthen model robustness. • Provided accurate object localization for real-time detection models. • Enhanced planogram compliance with pixel-level masks. • Improved robustness in rare product variant handling by 30%. • Contributed directly to mobile app deployment success.

For the Fooddy AI Dataset project, I annotated over 20,000 grocery items with bounding boxes and class labels for mobile object detection models. I performed pixel-level semantic segmentation for shelf analysis and automated inventory audits with high accuracy. My work also included automated validation for class imbalance and edge cases to strengthen model robustness. • Provided accurate object localization for real-time detection models. • Enhanced planogram compliance with pixel-level masks. • Improved robustness in rare product variant handling by 30%. • Contributed directly to mobile app deployment success.

2021 - 2024

Data Annotation Specialist

ImageClassification

During my tenure at Kiddies App as a Data Annotation Specialist, I annotated and categorized more than 5,000 children's product images for e-commerce recommendation models. I maintained strict adherence to age-appropriate and safety-compliance standards in image labeling. The role also involved content moderation and user behavior annotation to improve model predictions for user experience. • Labeled images for age-appropriateness and safety compliance. • Reviewed user-generated content for policy adherence and flagged inappropriate material. • Annotated clickstream and interaction data to optimize UX models. • Achieved high accuracy in minimizing false positives and boosting task completion predictions.

During my tenure at Kiddies App as a Data Annotation Specialist, I annotated and categorized more than 5,000 children's product images for e-commerce recommendation models. I maintained strict adherence to age-appropriate and safety-compliance standards in image labeling. The role also involved content moderation and user behavior annotation to improve model predictions for user experience. • Labeled images for age-appropriateness and safety compliance. • Reviewed user-generated content for policy adherence and flagged inappropriate material. • Annotated clickstream and interaction data to optimize UX models. • Achieved high accuracy in minimizing false positives and boosting task completion predictions.

2022 - 2022

Education

Work History