Dominio
Machine Learning & AI
Perfil de habilidad
Esta habilidad define expectativas en roles y niveles.
Roles
2
donde aparece esta habilidad
Niveles
5
ruta de crecimiento estructurada
Requisitos obligatorios
0
los otros 10 opcionales
Machine Learning & AI
Classical Machine Learning
22/2/2026
Selecciona tu nivel actual y compara las expectativas.
La tabla muestra cómo crece la profundidad desde Junior hasta Principal.
| Rol | Obligatorio | Descripción |
|---|---|---|
| Data Scientist | Writes clean Python code for data science: data processing functions, EDA scripts, simple ML pipelines. Uses list comprehensions, generators, decorators. Works with virtual environments and dependency management via pip/conda. | |
| LLM Engineer | Knows Python basics: data types, functions, classes, modules. Writes scripts for LLM API interaction, text data processing, and basic ML task automation under mentor guidance. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| Data Scientist | Develops production-ready Python code for ML systems. Applies OOP for structuring ML code, type hints for API documentation. Uses dataclasses, pydantic for data validation. Writes modular, testable code with proper error handling. | |
| LLM Engineer | Independently develops in Python for LLM projects: async/await for API calls, dataclasses for configurations, generators for data streaming. Writes unit tests and uses type hints. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| Data Scientist | Designs Python architecture for scalable ML systems. Optimizes performance through profiling, multiprocessing, async/await. Creates reusable packages and libraries for the data science team. Applies advanced patterns: metaclasses, context managers, descriptors. | |
| LLM Engineer | Designs Python applications for LLM: high-performance async servers, memory-efficient data processing, custom PyTorch extensions. Optimizes performance through profiling and concurrency. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| Data Scientist | Defines Python standards for the data science team: code style, architecture patterns, package management. Establishes shared Python infrastructure: internal packages, CI/CD templates, development tools. Conducts ML code architecture reviews. | |
| LLM Engineer | Defines Python standards for the LLM team. Establishes coding guidelines, project structure templates, CI/CD pipeline. Conducts architectural reviews of ML system Python components. |
| Rol | Obligatorio | Descripción |
|---|---|---|
| Data Scientist | Shapes Python engineering strategy for data science organizations. Defines tools and best practices for scaling Python ML development. Evaluates emerging tools (Ruff, uv, Mojo) and plans adoption strategy. | |
| LLM Engineer | Shapes enterprise Python strategy for ML/LLM organizations. Defines approaches to shared libraries, internal PyPI, and Python version management. Mentors leads on advanced Python techniques. |