Anastasiia S.

Key Skills

Software

Other

Top Subject Matter

LLM prompt quality and dataset evaluation (RL/LLM training data)

RLHF audio labeling for non-speech recognition

Translation quality assurance and linguistic review (pharmaceutical/technical)

Top Data Types

Text

Audio

Document

Top Task Types

Classification

Evaluation/Rating

Transcription

Text Summarization

Object Detection

RLHF

Freelancer Overview

LLM Prompt Quality Analyst (Amazon Prompt Upleveling Project). Brings 9+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Bachelor of Science, Arzobispo Loayza Institute (2023) and Bachelor of Science, LVIV State University of Life Safety (2018). AI-training focus includes data types such as Text and Audio and labeling workflows including Evaluation, Rating, and RLHF.

Labeling Experience

AI Data Annotator (RLHF – Key Audio Project)

AudioRLHF

Selected and annotated key audio segments to improve non-speech recognition in AI systems. Classified audio using structured categories such as music versus sound effects and foreground versus background. Followed RLHF-aligned labeling and consistency standards while anonymizing personal data in multimedia content. • Produced detailed audio descriptions including instruments, mood, genre, and production style. • Applied RLHF protocol requirements for labeling consistency. • Ensured privacy compliance via anonymization of personal data. • Contributed labeled audio data to machine learning training datasets.

2026 - Present

LLM Prompt Quality Analyst (Amazon Prompt Upleveling Project)

Text

Evaluated user prompts to improve training datasets for large language models using structured annotation criteria. Applied a framework covering domain classification, difficulty, realism, specificity, and verifiability to ensure consistent dataset quality. Provided clear reasoning to support annotation decisions and enhance reliability of model evaluation outcomes. • Assessed prompt quality, ambiguity, and suitability for dataset alignment. • Performed targeted rewrites to correct low-quality prompts. • Ensured consistency with strict production quality standards. • Documented justification for annotation decisions for auditability.

2026 - Present

AI Language Data Annotation Specialist

OtherText

Translated and evaluated reports and outreach materials to ensure clarity, tone, and cultural accuracy. Assessed language for contextual meaning, audience appropriateness, and readability, focusing on maintaining correct transfer of meaning across languages. Improved fluency and consistency of English-language content through careful language review. • Verified tone, register, and cultural alignment for target audiences. • Assessed contextual meaning and readability for multilingual materials. • Ensured accurate meaning transfer across multiple languages. • Improved consistency and fluency through linguistic editing.

2020 - 2020

Education

Work History