Fuhad Idris

Key Skills

Software

CVAT

Labelbox

Micro1

Telus

Top Subject Matter

Computer Vision (Video Annotation for Machine Learning)

Computer Vision (Image Annotation for Machine Learning)

Computer Vision (Video and Image Annotation)

Top Data Types

Video

Image

Text

Document

Top Task Types

Segmentation

Bounding Box

Classification

Freelancer Overview

AI Data Annotation Specialist (Video Segmentation Annotation). Core strengths include CVAT, Labelbox, and Micro1. Education includes Bachelor of Science, Lagos State University (2024). AI-training focus includes data types such as Video, Image, and Text and labeling workflows including Segmentation, Bounding Box, and Evaluation.

Labeling Experience

AI Data Annotation Specialist (Video Segmentation Annotation)

CVATVideoSegmentation

This role involved high-precision segmentation annotation of egocentric video data for computer vision model training. I identified and labeled object interactions, user actions, and temporal events using predefined taxonomies and structured annotation guidelines. My focus was on achieving accurate, consistent data labeling and ensuring adherence to quality assurance standards. • Carried out quality assurance to ensure correctness and consistency. • Flagged ambiguities, errors, and improved overall dataset quality. • Contributed to the development of datasets for AI model evaluation. • Applied large-scale annotation practices for machine learning systems.

This role involved high-precision segmentation annotation of egocentric video data for computer vision model training. I identified and labeled object interactions, user actions, and temporal events using predefined taxonomies and structured annotation guidelines. My focus was on achieving accurate, consistent data labeling and ensuring adherence to quality assurance standards. • Carried out quality assurance to ensure correctness and consistency. • Flagged ambiguities, errors, and improved overall dataset quality. • Contributed to the development of datasets for AI model evaluation. • Applied large-scale annotation practices for machine learning systems.

2025 - Present

AI Data Specialist (Structured and Unstructured Data Annotation)

TelusTextClassification

I annotated and validated structured and unstructured datasets for AI model training and evaluation at TELUS Digital AI Solution. My responsibilities included data labeling, quality review, and ensuring adherence to annotation guidelines. I contributed to increasing dataset accuracy and reliability by identifying inconsistencies and improving data quality. • Labeled and validated diverse dataset types for machine learning training. • Conducted quality review and guideline compliance checks. • Improved reliability by correcting inconsistencies and errors. • Supported project requirements through structured data annotation work.

I annotated and validated structured and unstructured datasets for AI model training and evaluation at TELUS Digital AI Solution. My responsibilities included data labeling, quality review, and ensuring adherence to annotation guidelines. I contributed to increasing dataset accuracy and reliability by identifying inconsistencies and improving data quality. • Labeled and validated diverse dataset types for machine learning training. • Conducted quality review and guideline compliance checks. • Improved reliability by correcting inconsistencies and errors. • Supported project requirements through structured data annotation work.

2025 - 2025

AI Quality Analyst (LLM Evaluation)

Micro1Text

As an AI quality analyst, I evaluated large language model (LLM) outputs by comparing model-generated responses for accuracy, reasoning, completeness, and instruction adherence. I identified logical errors, hallucinations, and missing context, providing structured explanations for my evaluations. My tasks contributed to ranking, grading, and improving LLM dataset quality for downstream performance. • Maintained high accuracy across large-scale evaluation datasets. • Identified and resolved LLM issues through judgment and feedback. • Analyzed reasoning quality and factual accuracy in AI responses. • Supported performance improvement with structured dataset analysis.

As an AI quality analyst, I evaluated large language model (LLM) outputs by comparing model-generated responses for accuracy, reasoning, completeness, and instruction adherence. I identified logical errors, hallucinations, and missing context, providing structured explanations for my evaluations. My tasks contributed to ranking, grading, and improving LLM dataset quality for downstream performance. • Maintained high accuracy across large-scale evaluation datasets. • Identified and resolved LLM issues through judgment and feedback. • Analyzed reasoning quality and factual accuracy in AI responses. • Supported performance improvement with structured dataset analysis.

2025 - 2025

Freelance Computer Vision Data Annotation (Video and Image)

LabelboxImageBounding Box

In this freelance role, I performed high-precision bounding box and segmentation annotation across video and image datasets for training computer vision models. I processed both video sequences and static images, focusing on object detection, tracking, and scene understanding using structured labeling techniques. My work involved annotation guideline application, dataset validation, and error correction to ensure high-quality, audit-ready outputs. • Used annotation tools like CVAT and Labelbox for structured outputs. • Identified and corrected labeling inconsistencies and errors. • Applied taxonomies to maintain data consistency and quality. • Validated dataset accuracy, completeness, and guideline adherence.

In this freelance role, I performed high-precision bounding box and segmentation annotation across video and image datasets for training computer vision models. I processed both video sequences and static images, focusing on object detection, tracking, and scene understanding using structured labeling techniques. My work involved annotation guideline application, dataset validation, and error correction to ensure high-quality, audit-ready outputs. • Used annotation tools like CVAT and Labelbox for structured outputs. • Identified and corrected labeling inconsistencies and errors. • Applied taxonomies to maintain data consistency and quality. • Validated dataset accuracy, completeness, and guideline adherence.

2025 - 2025

Image Annotation and Visual Annotation Specialist

CVATImageBounding Box

As an image annotation and visual annotation specialist, I annotated and categorized large-scale image datasets for computer vision model training. I applied bounding box and segmentation techniques using CVAT and Labelbox to improve dataset quality and reliability. I evaluated AI-generated images and flagged annotation errors to enhance structural consistency and correctness. • Conducted dataset quality checks for accuracy and consistency. • Identified and flagged visual artifacts and annotation errors. • Ensured instruction adherence through detailed reviews. • Improved dataset reliability by auditing and recategorizing images.

As an image annotation and visual annotation specialist, I annotated and categorized large-scale image datasets for computer vision model training. I applied bounding box and segmentation techniques using CVAT and Labelbox to improve dataset quality and reliability. I evaluated AI-generated images and flagged annotation errors to enhance structural consistency and correctness. • Conducted dataset quality checks for accuracy and consistency. • Identified and flagged visual artifacts and annotation errors. • Ensured instruction adherence through detailed reviews. • Improved dataset reliability by auditing and recategorizing images.

2024 - 2025

Education

Work History