Damola I.

Key Skills

Software

Internal/Proprietary Tooling

Top Subject Matter

Technology - AI Training, LLM Evaluation & Prompt/Response QA

Software - Coding Evaluation, QA Testing & Technical Documentation

Business/Education - Data Reporting, Research & Structured Content Review

Top Data Types

Text

Top Task Types

Classification

Freelancer Overview

I am a tech-focused AI task evaluator and freelance technical assistant with hands-on experience reviewing AI-generated responses, prompts, explanations, and code for clarity, correctness, completeness, safety, and instruction-following. My background includes AI response evaluation, prompt comparison, response rewriting, rubric-based quality review, and coding assessment across Java, Python, SQL, and JavaScript fundamentals. I am also comfortable with spreadsheets, structured documentation, Git/GitHub, Docker basics, and QA-style testing workflows. In my recent work, I have supported software and AI-related tasks by testing web app features, documenting API behavior, reproducing issues, and writing clear developer handover notes. I have also practiced data annotation, code evaluation, factual checks, edge-case analysis, and structured reporting through self-directed AI task practice and technical projects. My strengths are attention to detail, strong reasoning, clear communication, and the ability to apply evaluation rubrics consistently to improve training data quality. Reviewed AI responses for instruction-following, factual accuracy, tone, relevance, completeness, and reasoning quality. Compared alternative responses, ranked quality, rewrote weak outputs, and justified decisions using structured evaluation criteria. Evaluated code answers across Java, Python, SQL, and JavaScript fundamentals by checking syntax, logic, expected output, readability, and edge cases. Also reviewed AI-generated code suggestions and documented issues clearly for follow-up. Worked on data cleaning, spreadsheet-based reporting, web-app feature testing, API behavior notes, reproducible bug documentation, and structured technical handover notes to support quality professional workflows.

Labeling Experience

AI Response Evaluation & Prompt Review

TextClassification

Reviewed AI responses for instruction-following, factual accuracy, tone, relevance, completeness, and reasoning quality. Compared alternative responses, ranked quality, rewrote weak outputs, and justified decisions using structured evaluation criteria. Also evaluated AI-generated code suggestions for clarity, correctness, expected output, edge cases, and whether the answer solved the user’s problem.

2023 - Present

Education

Work History