Dawit Tsegaye

Key Skills

Software

Clickworker

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Bounding Box

Segmentation

Freelancer Overview

I am an AI/ML Engineer with hands-on experience in developing and deploying scalable machine learning solutions, with a strong focus on data labeling, annotation, and AI training data workflows. My background includes building NLP-driven chatbots, recommendation systems, and predictive models, where I have designed robust data pipelines and implemented advanced feature engineering, data preprocessing, and quality assurance techniques. I have worked extensively with Python, TensorFlow, PyTorch, Hugging Face, and cloud platforms, and am skilled in automating data annotation processes and integrating labeled data into production-ready ML models. My projects have involved real-time sentiment analysis, hybrid recommendation systems, and anomaly detection pipelines, all of which required meticulous attention to data quality and labeling accuracy. I am passionate about leveraging my expertise in data science and MLOps to ensure high-quality training data for impactful AI solutions.

Labeling Experience

Image Data Labelling

ClickworkerImageBounding BoxSegmentation

I worked on a large-scale image data labeling project focused on football players and football-related scenarios to support computer vision and object detection models. The work involved accurately annotating images by drawing bounding boxes around football players and key objects such as the ball and referees. The dataset included images from matches and training sessions with varying camera angles, lighting conditions, and levels of player occlusion. I followed strict annotation guidelines to ensure labeling consistency and high accuracy across the dataset. Quality control was a key part of the project, including reviewing annotations, correcting errors based on feedback, and meeting defined accuracy and productivity standards. The labeled data contributed to improving the performance of AI models used for sports analytics and automated visual recognition.

I worked on a large-scale image data labeling project focused on football players and football-related scenarios to support computer vision and object detection models. The work involved accurately annotating images by drawing bounding boxes around football players and key objects such as the ball and referees. The dataset included images from matches and training sessions with varying camera angles, lighting conditions, and levels of player occlusion. I followed strict annotation guidelines to ensure labeling consistency and high accuracy across the dataset. Quality control was a key part of the project, including reviewing annotations, correcting errors based on feedback, and meeting defined accuracy and productivity standards. The labeled data contributed to improving the performance of AI models used for sports analytics and automated visual recognition.

2021 - 2022

Education

Work History