Chambi Pendamani Kajiru

Key Skills

Software

AWS SageMaker

Anno-Mage

Appen

Data Annotation Tech

Label Studio

OneForma

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Image

Text

Video

Top Label Types

Action Recognition

Audio Recording

Classification

Computer Programming Coding

Cuboid

Data Collection

Evaluation Rating

Object Detection

Translation Localization

Freelancer Overview

I am a dedicated data and ICT professional with hands-on experience in data entry, health information systems, and database management, particularly within medical and public health domains. My background includes working with systems like DHIS2, eHMS, and Open EMR, where I ensured accurate data entry, quality assurance, and information confidentiality for patient records and health reports. I have developed and maintained custom data management solutions, including a Tracking Patient Monitoring System and Estate Management System, demonstrating my ability to handle large datasets and support system integration. I am proficient in data processing, verification, and analysis, and have strong skills in tools such as Microsoft Office, SPSS, and various programming languages. My experience in project coordination, system administration, and digital content creation further supports my ability to contribute to high-quality AI training data and annotation projects, especially those focused on healthcare and information systems. I am highly motivated, detail-oriented, and eager to leverage my expertise in data labeling and annotation for impactful AI and machine learning initiatives.

Labeling Experience

Image Labelling

Aws SagemakerImageBounding BoxRelationship

This project involves large-scale image labeling and annotation for AI and machine learning model training, primarily within the healthcare and ICT sectors. The scope of work includes labeling medical and technical images using bounding boxes, polygons, and classification techniques to accurately identify and categorize objects of interest. The dataset consists of thousands of images used to train and validate computer vision models for tasks such as object detection, image classification, and pattern recognition. I ensured high annotation accuracy by strictly following labeling guidelines, performing consistency checks, and reviewing annotations to meet quality standards. AWS SageMaker was used as the primary labeling platform to manage workflows, monitor task progress, and maintain annotation quality. Quality control measures included double-checking labeled data, resolving ambiguous cases, and adhering to project-specific accuracy thresholds to ensure reliable training data.

This project involves large-scale image labeling and annotation for AI and machine learning model training, primarily within the healthcare and ICT sectors. The scope of work includes labeling medical and technical images using bounding boxes, polygons, and classification techniques to accurately identify and categorize objects of interest. The dataset consists of thousands of images used to train and validate computer vision models for tasks such as object detection, image classification, and pattern recognition. I ensured high annotation accuracy by strictly following labeling guidelines, performing consistency checks, and reviewing annotations to meet quality standards. AWS SageMaker was used as the primary labeling platform to manage workflows, monitor task progress, and maintain annotation quality. Quality control measures included double-checking labeled data, resolving ambiguous cases, and adhering to project-specific accuracy thresholds to ensure reliable training data.

2019

Education

Work History