Patroba Oteko

Key Skills

Software

Label Studio

Sama

Internal/Proprietary Tooling

Top Subject Matter

No subject matter listed

Top Data Types

Audio

Computer Code Programming

Document

Image

Medical Dicom

Text

Video

Top Label Types

Classification

Computer Programming Coding

Data Collection

Entity Ner Classification

Evaluation Rating

Fine Tuning

Point Key Point

Prompt Response Writing SFT

Freelancer Overview

I am an AI training and data annotation specialist with over four years of hands-on experience supporting machine learning systems. My expertise includes large-scale text annotation, response evaluation, guideline-based scoring, and rigorous quality assurance for supervised and instruction-tuned models. I have consistently maintained high-quality standards, achieving 98%+ accuracy scores across multiple AI training projects, and have a strong track record in peer QA reviews to ensure data consistency and reduce labeling variance. My background in computer science and information security allows me to analyze structured outputs for accuracy, coherence, safety, and policy compliance while troubleshooting API-driven systems. I am adept at applying detailed annotation guidelines, documenting processes, and escalating issues as needed, making me a reliable contributor to AI and data labeling teams.

Labeling Experience

LLM Response Evaluation & Text Annotation Project

SamaTextEntity Ner Classification

Annotated and evaluated large-scale text datasets for supervised and instruction-tuned language models. Performed response grading, prompt–response alignment checks, taxonomy-based labeling, and peer QA reviews. Maintained 98%+ accuracy while adhering to strict quality guidelines and SLA-driven productivity benchmarks. Identified ambiguous or edge-case outputs to improve annotation clarity and model performance.

Annotated and evaluated large-scale text datasets for supervised and instruction-tuned language models. Performed response grading, prompt–response alignment checks, taxonomy-based labeling, and peer QA reviews. Maintained 98%+ accuracy while adhering to strict quality guidelines and SLA-driven productivity benchmarks. Identified ambiguous or edge-case outputs to improve annotation clarity and model performance.

2020 - 2024

Education

Work History