Gayatri Naga Bhudevi Sana

Key Skills

Software

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Image

Text

Top Label Types

Evaluation Rating

Computer Programming Coding

Freelancer Overview

I am a detail-oriented software engineer with hands-on experience in AI training data workflows, specializing in data annotation and evaluation of AI-generated mathematical solutions. At Outlier AI, I focused on assessing the logical accuracy of LLM outputs, providing structured feedback, and annotating mathematical datasets to enhance model reasoning and performance. My technical skills include Python, data structures, and algorithms, as well as expertise in AI response evaluation and mathematical reasoning validation. I have also developed end-to-end solutions in computer vision and authentication, such as a face recognition-based biometric login system, showcasing my ability to work with diverse data types and ensure high-quality labeled data for AI applications. I am eager to contribute my analytical mindset and technical expertise to improve the accuracy and reliability of AI systems through robust data labeling and annotation practices.

Labeling Experience

Mathematics sought AI Expert – AI Training, Outlier AI

Scale AITextEvaluation RatingComputer Programming Coding

In this role, I evaluated AI-generated mathematical solutions for accuracy and logical soundness. I provided structured feedback to enhance the reasoning capabilities and problem-solving performance of Large Language Models. I annotated mathematical datasets and identified reasoning flaws to improve label quality and AI output. • Conducted structured human evaluation of LLM-generated mathematics responses • Labeled and annotated mathematical data for use in supervised AI training • Supplied corrective feedback to address errors in AI solutions • Helped ensure consistently high-quality training data for model improvement

In this role, I evaluated AI-generated mathematical solutions for accuracy and logical soundness. I provided structured feedback to enhance the reasoning capabilities and problem-solving performance of Large Language Models. I annotated mathematical datasets and identified reasoning flaws to improve label quality and AI output. • Conducted structured human evaluation of LLM-generated mathematics responses • Labeled and annotated mathematical data for use in supervised AI training • Supplied corrective feedback to address errors in AI solutions • Helped ensure consistently high-quality training data for model improvement

2024 - 2025

Education

Work History