Skill-Profil

RAG

Dieser Skill definiert Erwartungen über Rollen und Level.

Machine Learning & AI LLM & Generative AI

Rollen

wo dieser Skill vorkommt

Stufen

strukturierter Entwicklungspfad

Pflichtanforderungen

die anderen 5 optional

Machine Learning & AI

LLM & Generative AI

22.2.2026

Wählen Sie Ihr aktuelles Level und vergleichen Sie die Erwartungen.

Was wird auf jedem Level erwartet

Die Tabelle zeigt, wie die Tiefe von Junior bis Principal wächst.

Rolle	Pflicht	Beschreibung
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.

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

Rolle	Pflicht	Beschreibung
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.

Rolle	Pflicht	Beschreibung
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.

Rolle	Pflicht	Beschreibung
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 Anforderungen

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 Anforderungen

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 Anforderungen

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 Anforderungen

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 Anforderungen

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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