Freelancer

Key Skills

Software

CVAT

Top Subject Matter

Computer Vision – Image & Video Annotation

Artificial Intelligence / Machine Learning

Data Labeling & Dataset Preparation

Top Data Types

Image

Text

Document

Top Task Types

Classification

Object Detection

Text Summarization

Bounding Box

Text Generation

Data Collection

Prompt + Response Writing (SFT)

Freelancer Overview

I have hands-on experience working with AI and machine learning projects that involved data preprocessing, feature engineering, model evaluation, and training workflows. During my experience as an AI & ML Developer at ShadowFox, I worked on image tagging models using datasets of over 5,000 images and performed data preprocessing and cross-validation on 10,000+ data samples to improve model performance and reliability. These tasks required attention to detail, data quality, and consistency, which are essential skills for data labeling and AI training. In addition, I have built AI-based applications such as an Adaptive Learning AI system and an AI Document Q&A system using RAG and prompt engineering concepts. My background in Python, machine learning, and LLM technologies enables me to understand how high-quality training data impacts AI performance. I am a quick learner, detail-oriented, and committed to delivering accurate and consistent annotations while following project guidelines.

Labeling Experience

YOLOv5 Object Detection Image Annotation Project

ImageBounding Box

Worked on an image annotation and data preparation project for training an object detection model using YOLOv5. The scope involved labelling images by drawing precise bounding boxes around multiple object categories to create high-quality training datasets for computer vision models. Tasks included: Annotating images with bounding boxes for object detection training Ensuring correct class labelling consistency across datasets Cleaning and validating image datasets to remove incorrect or low-quality samples Preparing structured datasets compatible with the YOLO format Improving dataset accuracy to enhance model performance and reduce false detections Quality measures followed: Strict adherence to labelling guidelines for consistency Cross-verification of annotations to minimise labelling errors Ensuring balanced class distribution in datasets Maintaining high precision in object localisation for training reliability

2025 - 2025

ShadowFox – Image Tagging & AI Training Data Preparation

ImageClassification

Worked on preparing and organizing image datasets for machine learning model training by performing image tagging, data preprocessing, validation, and quality checks. Reviewed image data for consistency, removed incorrect or duplicate samples, and ensured accurate categorization to improve dataset quality. Contributed to training and evaluating computer vision models using datasets containing over 5,000 images and participated in preprocessing and validation of 10,000+ data samples. Followed annotation guidelines, maintained labeling consistency, and supported model performance improvement through high-quality training data preparation and verification.

2025 - 2025

Education

Work History