Shivam Shivam

Key Skills

Software

Appen

Top Subject Matter

No subject matter listed

Top Data Types

Image

Top Label Types

Classification

Freelancer Overview

I have over three years of experience supporting IT and software delivery projects, where I developed a strong eye for detail and a high standard of accuracy—both critical for data labeling and AI training data work. In these roles, I was responsible for handling structured information, reviewing outputs for consistency, and ensuring that project deliverables met defined quality benchmarks. This experience trained me to work methodically, follow guidelines closely, and maintain focus on precision even when dealing with large volumes of data. My professional background includes managing Agile sprint workflows, coordinating with cross-functional teams, and maintaining clear, well-organized documentation using tools such as Jira, Confluence, and MS Project. I regularly tracked tasks, monitored progress, and prepared concise reports to support decision-making. These responsibilities strengthened my ability to maintain data integrity, spot inconsistencies early, and ensure that information remained accurate and up to date throughout the project lifecycle. Additionally, my hands-on experience with both web and mobile application projects has given me a solid understanding of technical requirements, quality standards, and end-to-end delivery processes. I am comfortable working with structured datasets, understanding system logic, and aligning outputs with predefined specifications. This combination of technical awareness, documentation discipline, and quality-focused execution makes me well-suited to support AI and machine learning initiatives that depend on reliable, well-annotated training data.

Labeling Experience

Appen

AppenImageClassification

This project focused on creating high-quality annotated image datasets to support machine learning models for image classification in the healthcare sector. The primary objective was to label and categorize medical images—such as X-rays, CT scans, MRI images, or dermatological images—into clinically relevant classes (e.g., normal vs. abnormal, disease type, severity level). The annotations were designed to help train and validate AI models used for early detection, diagnosis support, and clinical decision-making. The project involved reviewing large volumes of anonymized medical images and applying standardized annotation guidelines developed in collaboration with domain experts. Labels were assigned based on visual indicators, predefined medical criteria, and metadata provided with each image. Multiple quality-control steps were implemented, including cross-review, consistency checks, and error correction, to ensure annotation accuracy and reduce bias.

This project focused on creating high-quality annotated image datasets to support machine learning models for image classification in the healthcare sector. The primary objective was to label and categorize medical images—such as X-rays, CT scans, MRI images, or dermatological images—into clinically relevant classes (e.g., normal vs. abnormal, disease type, severity level). The annotations were designed to help train and validate AI models used for early detection, diagnosis support, and clinical decision-making. The project involved reviewing large volumes of anonymized medical images and applying standardized annotation guidelines developed in collaboration with domain experts. Labels were assigned based on visual indicators, predefined medical criteria, and metadata provided with each image. Multiple quality-control steps were implemented, including cross-review, consistency checks, and error correction, to ensure annotation accuracy and reduce bias.

2024 - 2025

Education

Work History