Rogers Ochieng

Key Skills

Software

Labelbox

Roboflow

Label Studio

Top Subject Matter

No subject matter listed

Top Data Types

Image

Video

Text

Top Label Types

Bounding Box

Segmentation

Classification

Tracking

Translation Localization

Point Key Point

Freelancer Overview

I am an experienced AI Training Specialist with over three years of hands-on expertise in data annotation and labeling for image, video, and audio datasets. My work spans computer vision tasks such as object detection, segmentation, and video object tracking, as well as linguistic data processing and audio transcription. I am highly proficient with industry-standard tools including Labelbox, YOLO, Roboflow, CVAT, and Supervisely, and I have a strong background in ensuring data quality through validation, guideline development, and dataset cleaning. My contributions to real-time object detection projects and video tracking for surveillance and traffic analysis have directly improved model accuracy and reduced error rates. I am committed to delivering high-quality, consistent datasets and thrive in collaborative, detail-oriented environments.

Labeling Experience

AI Training & Multi-Modal Data Annotation Specialist

Label StudioTextBounding BoxPoint Key Point

Led multi-modal AI data annotation projects involving image, video, audio, and text datasets for machine learning and deep learning model training. Performed bounding box, polygon, and semantic segmentation for object detection models, including YOLO-based systems. Conducted frame-by-frame video annotation and object tracking for surveillance and traffic monitoring datasets. Annotated and transcribed multilingual audio datasets, including speaker identification and emotion recognition tasks. Completed Named Entity Recognition (NER) and text classification for NLP model training. Managed datasets ranging from 10,000+ images to 5,000+ video sequences and 1,000+ hours of audio recordings.

Led multi-modal AI data annotation projects involving image, video, audio, and text datasets for machine learning and deep learning model training. Performed bounding box, polygon, and semantic segmentation for object detection models, including YOLO-based systems. Conducted frame-by-frame video annotation and object tracking for surveillance and traffic monitoring datasets. Annotated and transcribed multilingual audio datasets, including speaker identification and emotion recognition tasks. Completed Named Entity Recognition (NER) and text classification for NLP model training. Managed datasets ranging from 10,000+ images to 5,000+ video sequences and 1,000+ hours of audio recordings.

2022

AI Training & Multi-Modal Data Annotation Specialist

RoboflowVideoBounding BoxPoint Key Point

Worked as an AI Training Specialist handling large-scale image, video, audio, and text datasets for machine learning model development. Performed bounding box, polygon, semantic segmentation, and keypoint annotations for computer vision projects including object detection and tracking systems. Conducted frame-by-frame video tracking for moving objects in surveillance and traffic datasets. Annotated and transcribed multilingual audio datasets including speaker identification and emotion recognition tasks. Contributed to LLM fine-tuning through RLHF, prompt-response writing (SFT), evaluation, rating, and data validation. Managed dataset preprocessing and optimization using Roboflow and YOLO frameworks for model training. Ensured high-quality output through strict adherence to annotation guidelines, QA reviews, inter-annotator agreement checks, and consistency validation. Handled datasets ranging from 50,000+ images and 2,000+ hours of video and audio data across multiple AI project

Worked as an AI Training Specialist handling large-scale image, video, audio, and text datasets for machine learning model development. Performed bounding box, polygon, semantic segmentation, and keypoint annotations for computer vision projects including object detection and tracking systems. Conducted frame-by-frame video tracking for moving objects in surveillance and traffic datasets. Annotated and transcribed multilingual audio datasets including speaker identification and emotion recognition tasks. Contributed to LLM fine-tuning through RLHF, prompt-response writing (SFT), evaluation, rating, and data validation. Managed dataset preprocessing and optimization using Roboflow and YOLO frameworks for model training. Ensured high-quality output through strict adherence to annotation guidelines, QA reviews, inter-annotator agreement checks, and consistency validation. Handled datasets ranging from 50,000+ images and 2,000+ hours of video and audio data across multiple AI project

2022

AI Training & Multi-Modal Data Annotation Specialist

LabelboxImageBounding BoxSegmentation

Led and executed large-scale AI training data annotation projects across image, video, audio, and text datasets for machine learning and deep learning models. Performed bounding box, polygon, and semantic segmentation annotations for object detection models trained using YOLO frameworks. Conducted frame-by-frame video annotation and object tracking for surveillance and traffic monitoring systems. Annotated audio datasets including transcription, speaker identification, and emotion recognition to support speech-to-text and conversational AI systems. Executed Named Entity Recognition (NER), text classification, and prompt-response fine-tuning (SFT) tasks for Large Language Models (LLMs). Managed datasets exceeding 100,000+ annotated instances across multiple projects while maintaining over 98% annotation accuracy. Followed strict annotation guidelines, conducted quality assurance reviews, and implemented inter-annotator agreement checks to ensure consistency and high-quality outputs.

Led and executed large-scale AI training data annotation projects across image, video, audio, and text datasets for machine learning and deep learning models. Performed bounding box, polygon, and semantic segmentation annotations for object detection models trained using YOLO frameworks. Conducted frame-by-frame video annotation and object tracking for surveillance and traffic monitoring systems. Annotated audio datasets including transcription, speaker identification, and emotion recognition to support speech-to-text and conversational AI systems. Executed Named Entity Recognition (NER), text classification, and prompt-response fine-tuning (SFT) tasks for Large Language Models (LLMs). Managed datasets exceeding 100,000+ annotated instances across multiple projects while maintaining over 98% annotation accuracy. Followed strict annotation guidelines, conducted quality assurance reviews, and implemented inter-annotator agreement checks to ensure consistency and high-quality outputs.

2022

Education

Work History