Perfil de habilidad

RAG

Esta habilidad define expectativas en roles y niveles.

Machine Learning & AI LLM & Generative AI

Roles

donde aparece esta habilidad

Niveles

ruta de crecimiento estructurada

Requisitos obligatorios

los otros 5 opcionales

Machine Learning & AI

LLM & Generative AI

22/2/2026

Selecciona tu nivel actual y compara las expectativas.

Qué se espera en cada nivel

La tabla muestra cómo crece la profundidad desde Junior hasta Principal.

Rol	Obligatorio	Descripción
LLM Engineer		Knows RAG basics: retrieval + generation, basic pipeline with embedding and vector search. Builds simple RAG pipeline on LangChain with ChromaDB for QA task under mentor guidance.

Rol	Obligatorio	Descripción
LLM Engineer		Independently designs production RAG: advanced chunking, hybrid retrieval, re-ranking. Configures metadata filtering, conversation history, and source attribution. Evaluates quality via RAGAS.

Rol	Obligatorio	Descripción
LLM Engineer		Designs enterprise RAG systems: multi-source retrieval, agentic RAG, query routing. Optimizes retrieval quality through fine-tuning retrievers, custom re-rankers, and adaptive chunking strategies.

Rol	Obligatorio	Descripción
LLM Engineer		Defines RAG strategy for the LLM team. Establishes best practices for RAG architecture, data ingestion, quality monitoring. Coordinates RAG system as platform for multiple products.

Rol	Obligatorio	Descripción
LLM Engineer		Shapes enterprise RAG platform for the organization. Defines approaches to unified knowledge bases, multi-tenant RAG, and governance. Ensures scalability and quality at the scale of millions of documents.

Junior 1 requisitos

LLM Engineer

Knows RAG basics: retrieval + generation, basic pipeline with embedding and vector search. Builds simple RAG pipeline on LangChain with ChromaDB for QA task under mentor guidance.

Middle 1 requisitos

LLM Engineer

Independently designs production RAG: advanced chunking, hybrid retrieval, re-ranking. Configures metadata filtering, conversation history, and source attribution. Evaluates quality via RAGAS.

Senior 1 requisitos

LLM Engineer

Designs enterprise RAG systems: multi-source retrieval, agentic RAG, query routing. Optimizes retrieval quality through fine-tuning retrievers, custom re-rankers, and adaptive chunking strategies.

Lead / Staff 1 requisitos

LLM Engineer

Defines RAG strategy for the LLM team. Establishes best practices for RAG architecture, data ingestion, quality monitoring. Coordinates RAG system as platform for multiple products.

Principal 1 requisitos

LLM Engineer

Shapes enterprise RAG platform for the organization. Defines approaches to unified knowledge bases, multi-tenant RAG, and governance. Ensures scalability and quality at the scale of millions of documents.

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