Luis Perez

Key Skills

Software

Appen

Clickworker

Data Annotation Tech

Lionbridge

Remotasks

Scale AI

OneForma

Top Subject Matter

LLM evaluation in spanish

Chatbot security auditor

Audio comparison

Top Data Types

Document

Text

Top Label Types

Data Collection

Evaluation Rating

Question Answering

Text Generation

Text Summarization

Freelancer Overview

I am an experienced data annotation and AI training data specialist with a strong background in LLM evaluation, prompt engineering, and multilingual content QA. My expertise includes text and audio data labeling, intent classification, sentiment analysis, NER, and PII detection, with a focus on both Spanish (native) and English (C2) content. I have supervised and delivered high-quality annotations for conversational AI, NLP, and voice assistant projects, ensuring cultural adaptation and security compliance. Skilled in tools like OneForma, Appen, Lionbridge, and Scale AI, I excel at remote collaboration, guideline creation, and feedback writing. My work spans prompt generation, output evaluation, audio segmentation, and ethical risk analysis, contributing to the development and safety of advanced AI systems across diverse domains.

Labeling Experience

Audio Data Labeling & Quality Review for Speech AI

OneformaAudioTranslation LocalizationEvaluation Rating

Worked on large-scale audio annotation projects to support the training and evaluation of speech recognition and conversational AI systems. Tasks involved labeling speaker turns, transcriptions, audio segmentation, and intent classification to ensure data quality and usability for AI development. Responsibilities: Annotated and segmented audio clips to identify speaker changes, pauses, and overlaps. Verified and corrected transcriptions for accuracy, punctuation, and clarity. Labeled metadata including background noise, speech clarity, and emotional tone. Conducted QA reviews of peer-labeled files to ensure guideline consistency. Flagged audio with sensitive content, low quality, or unintelligible sections. Tools & Skills: Tools: [Insert platforms you’ve used: e.g., Toloka, Appen, Transcriber, Audacity, or custom labeling platforms]. Expertise: Attention to acoustic detail, multilingual audio annotation (Spanish & English), noise classification, and transcription QA.

Worked on large-scale audio annotation projects to support the training and evaluation of speech recognition and conversational AI systems. Tasks involved labeling speaker turns, transcriptions, audio segmentation, and intent classification to ensure data quality and usability for AI development. Responsibilities: Annotated and segmented audio clips to identify speaker changes, pauses, and overlaps. Verified and corrected transcriptions for accuracy, punctuation, and clarity. Labeled metadata including background noise, speech clarity, and emotional tone. Conducted QA reviews of peer-labeled files to ensure guideline consistency. Flagged audio with sensitive content, low quality, or unintelligible sections. Tools & Skills: Tools: [Insert platforms you’ve used: e.g., Toloka, Appen, Transcriber, Audacity, or custom labeling platforms]. Expertise: Attention to acoustic detail, multilingual audio annotation (Spanish & English), noise classification, and transcription QA.

2019

AI Data Annotation & Quality Review for NLP Models

Data Annotation TechTextText Generation

Contributed to a large-scale data labeling project focused on training Natural Language Processing (NLP) models. Tasks included annotating text data, categorizing intent, evaluating AI-generated responses, and ensuring compliance with project-specific quality standards. Responsibilities: Annotated text datasets with labels for sentiment, intent, and entity recognition. Performed quality assurance on peer annotations to ensure accuracy and consistency. Reviewed AI model outputs, identifying factual errors, hallucinations, and inappropriate content. Collaborated with remote teams to clarify guidelines and improve annotation consistency. Tools & Skills: Platforms: [Toloka / Appen / Welocalize / Mindrift / Label Studio / Prodigy] (customize with the tools you’ve actually used). Skills: Attention to detail, guideline adherence, logical categorization, and critical evaluation of AI-generated text. Languages: Native Spanish (LATAM), fluent English (C2).

Contributed to a large-scale data labeling project focused on training Natural Language Processing (NLP) models. Tasks included annotating text data, categorizing intent, evaluating AI-generated responses, and ensuring compliance with project-specific quality standards. Responsibilities: Annotated text datasets with labels for sentiment, intent, and entity recognition. Performed quality assurance on peer annotations to ensure accuracy and consistency. Reviewed AI model outputs, identifying factual errors, hallucinations, and inappropriate content. Collaborated with remote teams to clarify guidelines and improve annotation consistency. Tools & Skills: Platforms: [Toloka / Appen / Welocalize / Mindrift / Label Studio / Prodigy] (customize with the tools you’ve actually used). Skills: Attention to detail, guideline adherence, logical categorization, and critical evaluation of AI-generated text. Languages: Native Spanish (LATAM), fluent English (C2).

2021 - 2024

Education

Work History