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Key Skills
Software
Google Cloud Vertex AI
Labelbox
Scale AITop Subject Matter
No subject matter listed
Top Data Types
Text
Computer Code ProgrammingTop Task Types
Classification
Entity (NER) Classification
Computer Programming/Coding
Prompt + Response Writing (SFT)
Function CallingFreelancer Overview
As an Applied AI Research Engineer and Software Engineer, my experience with AI training data spans from processing complex, unstructured multimodal datasets to engineering automated, high-fidelity data validation pipelines. During my tenure at the Universidad Tecnológica de Bolívar, I co-authored peer-reviewed AI research by building multimodal tooling that fused and structured diverse data types, including video (head pose), audio (Librosa), and speech-to-text NLP data. This background gives me a deep understanding of feature extraction, dataset curation, and the meticulous alignment required to prepare high-quality data for sensitive analytical models. What sets me apart from traditional data annotators is my ability to programmatically automate, clean, and validate training data at scale. I architected Autumn, a concurrent AI scraping agent utilizing Pydantic AI ReAct loops and FastAPI to extract unstructured web data and output it into strictly validated, structured JSON schemas. Proficient in Python, NLP fundamentals, and LLM orchestration, I bring a highly technical engineering approach to data annotation, allowing me to build the automated pipelines and programmatic validation layers necessary to train enterprise-grade AI systems.