Jesus G.

Key Skills

Software

Other

Top Subject Matter

AI systems evaluation and quality assurance for AI-driven products

web platform testing

Technical troubleshooting fundamentals applicable to edge-case and ambiguity handling for AI/data QA

Top Data Types

Image

Text

Audio

Top Task Types

Classification

Freelancer Overview

Linux Systems & Technical Troubleshooting — Ongoing (hobby/independent). Brings 7+ years of professional experience across complex professional workflows, research, and quality-focused execution. Core strengths include Internal, Proprietary Tooling, and Other. Education includes Freelance Software Developer & AI Systems Evaluator, Self-Employed (2023). AI-training focus includes data types such as Computer Code and Programming and labeling workflows including Evaluation, Rating, and Classification.

Labeling Experience

Linux Systems & Technical Troubleshooting — Ongoing (hobby/independent)

Other

Applied methodical variable isolation and reproducible documentation practices to diagnose complex technical failures. Translated troubleshooting skills into an ability to identify edge cases and ambiguous items relevant to data quality and labeling consistency. Ensured outcomes were consistent and explainable by recording configuration decisions and steps to reproduce results. • Diagnosed kernel panics, boot failures, and display manager conflicts • Isolated variables to locate low-signal faults • Documented reproducible fixes and configuration decisions clearly • Emphasized consistent, explainable outcomes that parallel labeling QA rigor

2024 - Present

Freelance Software Developer & AI Systems Evaluator (Self-Employed) — 2023 to Present

Classification

Evaluated AI-driven tools by running structured exploratory and edge-case testing to identify failures at prompt and interface boundaries. Documented detected issues with clear reproduction steps and used consistent test criteria to assess outcomes against expectations. Applied rubric-like guideline reasoning to categorize observed behaviors and validate that results met defined standards across repetitive evaluation scenarios. • Tested third-party AI study tools for prompt-scoping gaps and UI/CSS rendering defects • Ran manual QA passes across multiple production web platforms using consistent criteria • Built and maintained tooling/workflows to support processing, labeling, and triage of large volumes of data (described at the role level) • Followed strict data-handling and security protocols to preserve integrity of evaluation data and credentials

2023 - Present

Education

Work History