Joel Ruiz

Key Skills

Software

AWS SageMaker

Data Annotation Tech

Google Cloud Vertex AI

SuperAnnotate

Internal/Proprietary Tooling

Appen

Top Subject Matter

No subject matter listed

Top Data Types

3D Sensor

Computer Code Programming

Document

Top Task Types

Bounding Box

Computer Programming Coding

Fine Tuning

Prompt Response Writing SFT

Freelancer Overview

I am an experienced AI Data Specialist with four years of hands-on expertise in large language model (LLM) evaluation, data annotation, and multilingual dataset QA. My background in software engineering and legal transcription has given me an exceptional eye for precision, structure, and language accuracy. I’ve worked across English, Spanish, and French datasets, evaluating prompts and responses for correctness, clarity, and safety while designing custom rubrics and validation frameworks to maintain over 99% quality consistency. Beyond production-level annotation, I bring technical insight from coding and financial AI systems, allowing me to evaluate model outputs from both linguistic and logical perspectives. I’m skilled in rubric creation, fine-tuning QA, and multi-turn conversation testing. My focus is to deliver high-quality, bias-free, and audit-ready training data that helps AI systems reason more accurately and communicate naturally.

Labeling Experience

AI Data Engineer – LLM Evaluation & Financial Model QA

Data Annotation TechTextQuestion AnsweringText Summarization

Reviewed and scored large language model (LLM) outputs across English, Spanish, and French datasets. Applied structured rubrics to measure helpfulness, factuality, coherence, and safety compliance. Authored and refined prompts for red-teaming exercises, identifying potential policy violations and recommending safer rewrites. Collaborated with cross-functional teams to ensure consistent scoring and bias mitigation. Achieved over 99% QA pass rate and contributed to improved LLM alignment and dataset integrity.

Reviewed and scored large language model (LLM) outputs across English, Spanish, and French datasets. Applied structured rubrics to measure helpfulness, factuality, coherence, and safety compliance. Authored and refined prompts for red-teaming exercises, identifying potential policy violations and recommending safer rewrites. Collaborated with cross-functional teams to ensure consistent scoring and bias mitigation. Achieved over 99% QA pass rate and contributed to improved LLM alignment and dataset integrity.

2025

Software Engineering Data Annotator – Code and Model Evaluation

AppenComputer Code ProgrammingClassificationText Generation

Worked on AI model evaluation projects focused on programming and code understanding tasks. Reviewed and tested machine-generated code in multiple languages, including Python, C#, and JavaScript, verifying functionality, syntax accuracy, and adherence to software engineering best practices. Designed evaluation rubrics for function-calling accuracy, algorithm efficiency, and documentation quality. Collaborated with other annotators to improve model consistency and debugging accuracy across software-related datasets. Delivered structured reports that helped enhance model performance in real-world coding environments.

Worked on AI model evaluation projects focused on programming and code understanding tasks. Reviewed and tested machine-generated code in multiple languages, including Python, C#, and JavaScript, verifying functionality, syntax accuracy, and adherence to software engineering best practices. Designed evaluation rubrics for function-calling accuracy, algorithm efficiency, and documentation quality. Collaborated with other annotators to improve model consistency and debugging accuracy across software-related datasets. Delivered structured reports that helped enhance model performance in real-world coding environments.

2024

Education

Work History