Dominio
Machine Learning & AI
Perfil de habilidad
Embeddings, fine-tuning, multi-label classification, few-shot learning, benchmarks
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
Selecciona tu nivel actual y compara las expectativas.
La tabla muestra cómo crece la profundidad desde Junior hasta Principal.
| Rol | Obligatorio | Descripción |
|---|---|---|
| NLP Engineer | Obligatorio | Knows text classification basics: bag-of-words, TF-IDF, basic classifiers. Trains simple models for text categorization, spam filtering. Evaluates via accuracy, F1-score. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| NLP Engineer | Obligatorio | Independently develops text classification systems: fine-tuning BERT/RoBERTa, zero-shot classification via LLM, multi-label classification. Works with imbalanced datasets. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| NLP Engineer | Obligatorio | Designs production text classification systems: hierarchical classification, dynamic taxonomy, continual learning. Optimizes for high throughput and low latency in production. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| NLP Engineer | Obligatorio | Defines text classification strategy for the team. Establishes taxonomy management processes, evaluation standards, and architectural decisions for classification-based NLP products. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| NLP Engineer | Obligatorio | Shapes enterprise text classification strategy. Defines unified taxonomy, cross-domain classification approaches, and standards for all classification-based NLP products. |