Karthiyayini J

Key Skills

Software

Appen

Data Annotation Tech

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Top Label Types

Classification

Translation Localization

Evaluation Rating

Freelancer Overview

With over 18 years of experience in training, quality management, and knowledge base development, I specialize in creating and optimizing processes that support high-quality data labeling and AI training data initiatives. I have led teams in designing SOPs, developing comprehensive knowledge repositories, and implementing quality audit procedures to ensure data accuracy and consistency. My work has included deploying machine learning techniques to streamline manual efforts, developing user-facing scripts for various channels, and collaborating on product development to enhance user experience. I am adept at managing large-scale content and customer experience projects, including the launch of web-based knowledge bases with 150+ SOPs and leading international training programs. My focus on process excellence, quality assurance, and user-centric content development positions me strongly for roles in data annotation, data labeling, and AI training data management across diverse domains.

Labeling Experience

AI Annotation

Data Annotation TechTextClassificationTranslation Localization

The project was aimed to assess the model’s ability to use contextual web information to produce high-quality, trustworthy responses. The task was to evaluate and rate responses provided AI across various topics in reference to the rubrics, to ensure they meet the standards for native fluency as well content accuracy. The following aspects were checked during this evaluation: Instruction Following Content Conciseness & Relevance Content Completeness Context Utilization Completeness and Relevance Collaborativity Groundedness & Truthfulness Links Overall Response Quality Final quality rating

The project was aimed to assess the model’s ability to use contextual web information to produce high-quality, trustworthy responses. The task was to evaluate and rate responses provided AI across various topics in reference to the rubrics, to ensure they meet the standards for native fluency as well content accuracy. The following aspects were checked during this evaluation: Instruction Following Content Conciseness & Relevance Content Completeness Context Utilization Completeness and Relevance Collaborativity Groundedness & Truthfulness Links Overall Response Quality Final quality rating

2025 - 2025

Education

Work History