Dominio
Machine Learning & AI
Perfil de habilidad
Sequence labeling, SpaCy, custom entities, BIO tagging, tokenization
Roles
1
donde aparece esta habilidad
Niveles
5
ruta de crecimiento estructurada
Requisitos obligatorios
5
los otros 0 opcionales
Machine Learning & AI
Natural Language Processing
17/3/2026
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La tabla muestra cómo crece la profundidad desde Junior hasta Principal.
| Rol | Obligatorio | Descripción |
|---|---|---|
| NLP Engineer | Obligatorio | Knows NER basics: entity types (PER, ORG, LOC), BIO tagging, basic approaches. Applies pre-trained spaCy NER models and evaluates quality via F1-score. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| NLP Engineer | Obligatorio | Independently trains and fine-tunes NER models for domain-specific tasks. Annotates data, configures BIO/BILOU schemes, trains models on spaCy and Hugging Face transformers. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| NLP Engineer | Obligatorio | Designs production NER systems: multi-model ensemble, active learning for annotation, nested NER, cross-lingual transfer. Optimizes for high accuracy on domain-specific data. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| NLP Engineer | Obligatorio | Defines NER strategy for the team. Establishes guidelines for annotation, model selection, evaluation methodology. Coordinates annotator work and ensures labeling consistency. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| NLP Engineer | Obligatorio | Shapes enterprise NER strategy for the organization. Defines unified entity taxonomy, cross-domain NER approaches, and quality assurance standards for all company NER systems. |