Obwaya J.

Key Skills

Software

Clickworker

CloudFactory

Data Annotation Tech

Micro1

Remotasks

Top Subject Matter

AI output evaluation and guideline-based quality control

AI data annotation and dataset labeling

LLM prompt engineering and response optimization

Top Data Types

Text

Image

Top Task Types

Data Collection

Prompt + Response Writing (SFT)

Object Detection

Classification

Fine-tuning

Question Answering

Freelancer Overview

AI Trainer & Data Annotation Specialist. Brings 9+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include N, A, and ChatGPT. Education includes Bachelor of Science, University of Eldoret and Cisco Certified Network Associate, Alphax College. AI-training focus includes data types such as Text and labeling workflows including Evaluation, Rating, and Data Collection.

Labeling Experience

AI Output Evaluation & Review (Project Work)

TextPrompt + Response Writing (SFT)

Performed prompt engineering and response optimization as part of improving AI outputs during evaluation and iterative rewriting. Used structured reasoning and feedback to compare, rewrite, and optimize responses for clarity, correctness, and user usefulness. • Optimized prompts to elicit higher-quality outputs • Rewrote and refined responses based on evaluation feedback • Compared and ranked responses by quality and correctness • Applied multi-step reasoning to assess response effectiveness

2023 - Present

AI Trainer & Data Annotation Specialist (Annotation)

TextData Collection

Annotated datasets with a focus on high accuracy and consistency for AI training and evaluation. Worked with both text and image-related annotation tasks by applying labeling standards and quality checks aligned with project guidelines. • Performed text dataset annotation for training/evaluation • Applied quality assurance checks for consistency • Supported dataset refinement through feedback loops • Ensured labeled outputs adhered to provided guidelines

2023 - Present

AI Trainer & Data Annotation Specialist

Text

Evaluated AI-generated outputs using structured guidelines, rubrics, and taxonomy-based systems to ensure quality and consistency. Identified edge cases, ambiguities, and weak or inconsistent reasoning in model responses, then provided structured feedback to improve performance. • Used rubrics to rate and compare response quality • Applied taxonomy-based classification to refine outputs • Conducted error detection across edge cases and ambiguity • Provided structured feedback for iterative improvements

2023 - Present

Education

Work History