Ritesh Raj

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Top Label Types

Text Generation

Emotion Recognition

Evaluation Rating

Data Collection

Prompt Response Writing SFT

Freelancer Overview

I am an experienced AI trainer and data labeling specialist with a strong background in evaluating and annotating text data for large language models. My work has focused on assessing AI-generated responses for accuracy, relevance, and compliance with detailed guidelines, as well as identifying hallucinations, factual errors, and bias. I have hands-on experience with tools like ChatGPT, GitHub Copilot, and Perplexity.ai, and am skilled in text categorization, instruction-following assessment, and structured quality review. My approach combines high attention to detail, logical reasoning, and a commitment to consistency, ensuring reliable and high-quality training data for NLP applications. I have contributed to projects in domains such as general knowledge, instruction validation, and user interaction analysis, and have been recognized for my accuracy and quality-driven work.

Labeling Experience

LLM Evaluator

Scale AIComputer Code ProgrammingText GenerationEmotion Recognition

Performed structured text data labeling and LLM response evaluation according to strict project guidelines. Tasks included text categorization, evaluating AI-generated responses for relevance, factual accuracy, instruction adherence, and clarity, as well as ranking multiple outputs based on quality criteria. Identified hallucinations, inconsistencies, and guideline violations while maintaining consistency across high-volume annotation work.

Performed structured text data labeling and LLM response evaluation according to strict project guidelines. Tasks included text categorization, evaluating AI-generated responses for relevance, factual accuracy, instruction adherence, and clarity, as well as ranking multiple outputs based on quality criteria. Identified hallucinations, inconsistencies, and guideline violations while maintaining consistency across high-volume annotation work.

2019

Education

Work History