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R
Rume Brughs

Rume Brughs

AI Data Labeling Specialist (Engineering/Inventory)

Chile flagSantiago, Chile
$15.00/hrExpertLabel StudioLabelboxScale AI

Key Skills

Software

Label StudioLabel Studio
LabelboxLabelbox
Scale AIScale AI
Other

Top Subject Matter

Engineering/Inventory Data
Spanish (LATAM) Inventory Management/LLM
Human-in-the-Loop (HITL) AI Feedback Systems

Top Data Types

TextText

Top Task Types

ClassificationClassification
Entity (NER) ClassificationEntity (NER) Classification
Prompt + Response Writing (SFT)Prompt + Response Writing (SFT)
Red TeamingRed Teaming

Freelancer Overview

AI Data Labeling Specialist (Engineering/Inventory). Core strengths include Internal, Proprietary Tooling, and Other. AI-training focus includes data types such as Text and labeling workflows including Entity (NER) Classification, Prompt + Response Writing (SFT), and Evaluation.

ExpertEnglishSpanish

Labeling Experience

Specialist, Spanish-Language LLM Fine-Tuning and SFT

OtherTextPrompt Response Writing SFT
Provided specialized input in Spanish (LATAM) for AI response tuning, focusing on inventory management. Contributed to supervised fine-tuning (SFT) of LLMs for Spanish regional usage. Supported accurate adaptation of AI outputs to market requirements. • Developed prompts and answers for inventory management use cases • Evaluated and rated AI-generated responses • Focused on the use of Spanish (LATAM) terminology in datasets • Enhanced output quality for regional accuracy

Provided specialized input in Spanish (LATAM) for AI response tuning, focusing on inventory management. Contributed to supervised fine-tuning (SFT) of LLMs for Spanish regional usage. Supported accurate adaptation of AI outputs to market requirements. • Developed prompts and answers for inventory management use cases • Evaluated and rated AI-generated responses • Focused on the use of Spanish (LATAM) terminology in datasets • Enhanced output quality for regional accuracy

2023 - Present

AI Data Labeling Specialist (Engineering/Inventory)

TextEntity Ner Classification
Developed logic for the accurate extraction of entities from complex engineering and inventory data strings. Implemented workflows for annotating unstructured text into structured JSON schema for inventory, finance, and task tracking. Led efforts to refine data annotation processes to improve model training and data accuracy. • Designed custom annotation flows for Text categorization • Focused on extraction and labeling in the engineering/inventory sector • Used knowledge of TypeScript, C#, and real-time systems to optimize workflows • Collaborated with AI teams to evaluate annotation quality

Developed logic for the accurate extraction of entities from complex engineering and inventory data strings. Implemented workflows for annotating unstructured text into structured JSON schema for inventory, finance, and task tracking. Led efforts to refine data annotation processes to improve model training and data accuracy. • Designed custom annotation flows for Text categorization • Focused on extraction and labeling in the engineering/inventory sector • Used knowledge of TypeScript, C#, and real-time systems to optimize workflows • Collaborated with AI teams to evaluate annotation quality

2023 - Present

Customer Review Sentiment Labeling

TextClassification
I developed a text annotation project focused on labeling customers reviews in the food and restaurant domain. The goal was to classify each review into positive, negative or nuetral sentiment. I created clear annotation guidlines to ensure consistency, including how to handle ambiguous or mixed opinions. i labeled a dataset of over 50 - 100 reviews and included confidence scores for quality control. I performed a review pass to identify and correct inconsistencies, ensuring high-quality labeled data suitable for training machine leaning models.

I developed a text annotation project focused on labeling customers reviews in the food and restaurant domain. The goal was to classify each review into positive, negative or nuetral sentiment. I created clear annotation guidlines to ensure consistency, including how to handle ambiguous or mixed opinions. i labeled a dataset of over 50 - 100 reviews and included confidence scores for quality control. I performed a review pass to identify and correct inconsistencies, ensuring high-quality labeled data suitable for training machine leaning models.

2025 - 2025

Red Teaming AI Annotation Specialist

TextRed Teaming
Evaluated large language model (LLM) prompts and outputs for accuracy, safety, and determinism in red teaming environments. Labeled and tested AI responses with emphasis on data security and prompt injection mitigation. Designed and reviewed structured prompt/response suites for security testing. • Created and tested red teaming prompt sets • Focused on LLM safety and reliability • Developed annotation guidelines for penetration testing • Worked with security and AI teams to assess risk

Evaluated large language model (LLM) prompts and outputs for accuracy, safety, and determinism in red teaming environments. Labeled and tested AI responses with emphasis on data security and prompt injection mitigation. Designed and reviewed structured prompt/response suites for security testing. • Created and tested red teaming prompt sets • Focused on LLM safety and reliability • Developed annotation guidelines for penetration testing • Worked with security and AI teams to assess risk

2023 - 2023

System Architect, Human-in-the-Loop (HITL) AI Feedback

Text
Constructed feedback systems utilizing human-in-the-loop methods for accuracy improvement. Managed annotation and evaluation workflows supporting iterative AI model enhancement. Analyzed feedback cycles to maximize training data reliability. • Designed HITL feedback annotation schema • Oversaw iterative cycles of annotation/evaluation • Applied techniques for tracking decision accuracy • Improved precision of AI training data

Constructed feedback systems utilizing human-in-the-loop methods for accuracy improvement. Managed annotation and evaluation workflows supporting iterative AI model enhancement. Analyzed feedback cycles to maximize training data reliability. • Designed HITL feedback annotation schema • Oversaw iterative cycles of annotation/evaluation • Applied techniques for tracking decision accuracy • Improved precision of AI training data

2022 - 2023

Education

C

Caritas University Enugu

Bachelor of Science, Computer Science

Bachelor of Science
2007 - 2011
U

Universidad Nacional de San Luis

Master of Science, Software Quality

Master of Science
2025

Work History

I

Instituto Nacional de Estadísticas

IT Manager

Santiago
2023 - 2023
G

Gran Calafate

Inventory Manager

Coyhaique
2021 - 2021