Skill Profile

RAG

This skill defines expectations across roles and levels.

Machine Learning & AI LLM & Generative AI

Roles

where this skill appears

Levels

structured growth path

Mandatory requirements

the other 5 optional

Machine Learning & AI

LLM & Generative AI

2/22/2026

Choose your current level and compare expectations. The items below show what to cover to advance to the next level.

What is Expected at Each Level

The table shows how skill depth grows from Junior to Principal. Click a row to see details.

Role	Required	Description
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.

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

Role	Required	Description
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.

Role	Required	Description
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.

Role	Required	Description
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 requirements

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 requirements

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 requirements

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 requirements

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 requirements

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.

Loading comments...