Freelancer

Key Skills

Software

Appen

Don't disclose

Top Subject Matter

LLM evaluation and multilingual dataset QA

Search engine data quality evaluation and intent/relevance labeling

Clinical Taxonomy and Evidence-Based Medicine

Top Data Types

Text

Image

Top Task Types

Classification

Question Answering

Data Collection

RLHF

Entity (NER) Classification

Red Teaming

Freelancer Overview

RLHF Specialist / Domain Annotator at CENTAUR.AI (Remote). Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Don't disclose and Appen. Education includes Doctor of Medicine, Vinnytsia National Pirogov Medical University (VNMU) (2020). AI-training focus includes data types such as Text and labeling workflows including RLHF, Entity (NER) Classification, and Fine Tuning.

Labeling Experience

RLHF Specialist / Domain Annotator | May

AppenTextRLHF

No description provided.

2026 - Present

AI Data Specialist (LLM Evaluator) - Centaur.Ai

AppenTextRLHFRed Teaming

Evaluated Large Language Model outputs to assess logical consistency, factual adherence, and semantic drift across multilingual text datasets. Applied internal quality assurance rubrics to determine acceptance outcomes while maintaining high throughput in a remote setting. Generated and used complex multi-turn prompt scenarios to probe model boundary conditions and edge-case reasoning. • Assessed multilingual LLM responses for consistency and factuality. • Rated outputs against strict internal QA rubric criteria. • Tested reasoning capabilities using boundary/edge-case prompt + response evaluations. • Maintained a >98% acceptance rate under high-volume review workflows.

2026 - Present

AI Data Specialist & LLM Evaluator at Centaur.ai (Remote)

Don't discloseTextRLHF

Provided quality ratings and evaluations of Large Language Model outputs for consistency, factual adherence, and semantic drift across multilingual datasets. Applied internal rubric criteria to judge whether model responses matched expected reasoning and factual constraints. Maintained strict quality thresholds to ensure reliable dataset signal for downstream model improvement. • Rated logical consistency and detected semantic drift in model responses • Checked factual adherence relative to expected knowledge and context • Designed complex multi-turn prompts to test edge cases and reasoning boundaries • Sustained a >98% acceptance rate under high-throughput QA requirements

2026 - Present

Linguistic Data Annotator at APPEN (Remote)

AppenTextEntity (NER) Classification

You performed data annotation on Slavic-language text corpora for NLP training. Your labeling included named entity recognition, query categorization, and sentiment tagging. You also added linguistic-quality awareness by identifying semantic drift and slang evolution that could make training data obsolete. • Labeled named entities in raw text • Categorized queries according to annotation guidelines • Tagged sentiment for supervised learning • Identified regional semantic drift and slang evolution for dataset longevity

2021 - Present

Search Engine & Data Quality Evaluator at Appen (Remote)

AppenTextClassification

Labeled and evaluated search engine data quality by rating how well web search query intents matched scraped landing page relevance. Used a 150+ page standardized guideline to apply consistent scoring across tasks. Helped train ranking-related components by clarifying ambiguous linguistic inputs in high-noise scenarios. • Rated query intent versus landing page relevance using long-form guideline criteria • Disambiguated noisy or ambiguous queries to improve training signal • Completed tasks to meet weekly volumetric quotas • Maintained top-tier accuracy metrics while labeling at scale

2021 - Present

Education

Work History