Prashanth Hosamani

Key Skills

Software

Appen

Data Annotation Tech

Mindrift

Remotasks

Scale AI

Top Subject Matter

No subject matter listed

Top Data Types

Computer Code Programming

Document

Text

Top Task Types

Computer Programming/Coding

Data Collection

Evaluation/Rating

Prompt + Response Writing (SFT)

RLHF

Freelancer Overview

I have hands-on experience in AI training and data labeling through my role as an AI Model Trainer at Outlier AI (Scale AI). My work involved evaluating large language model outputs for accuracy, relevance, and alignment with user intent, as well as designing and optimizing prompts to enhance performance across diverse tasks. I also served as a feedback reviewer for peer trainers, ensuring quality, consistency, and adherence to evaluation guidelines, while collaborating with cross-functional AI teams to improve datasets and workflows. With a strong foundation in Python, SQL, and AI integration, I bring both technical and analytical skills to annotation projects. My background spans natural language processing, model evaluation, and data quality assurance, supported by projects in NLP applications, AI chatbots, churn prediction, and data analysis. I excel in detail-oriented environments, quickly adapt to new subject matter, and am motivated to contribute to building reliable, high-quality AI systems.

Labeling Experience

AI Model Trainer

Scale AITextRLHFEvaluation Rating

• Evaluated AI model outputs for accuracy, relevance, and alignment with user intent, providing structured feedback to refine large language models. • Designed and optimized prompts to improve model performance and generate higher-quality responses across diverse use cases. • Acted as a feedback reviewer for peer trainers, ensuring consistency, quality, and adherence to evaluation guidelines. • Collaborated with cross-functional AI teams to enhance training datasets and improve evaluation workflows.

• Evaluated AI model outputs for accuracy, relevance, and alignment with user intent, providing structured feedback to refine large language models. • Designed and optimized prompts to improve model performance and generate higher-quality responses across diverse use cases. • Acted as a feedback reviewer for peer trainers, ensuring consistency, quality, and adherence to evaluation guidelines. • Collaborated with cross-functional AI teams to enhance training datasets and improve evaluation workflows.

2024 - 2024

Education

Work History