Skill-Profil

Embeddings & Vector DB

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 text embeddings and vector database basics. Generates embeddings via sentence-transformers, stores and searches in ChromaDB. Understands cosine similarity and basic semantic search.

Rolle	Pflicht	Beschreibung
LLM Engineer		Independently designs embedding pipelines: model selection (OpenAI, Cohere, BGE), chunking strategies, and metadata filtering. Configures Pinecone/Weaviate for production workloads with recall optimization.

Rolle	Pflicht	Beschreibung
LLM Engineer		Designs scalable embedding infrastructure: hybrid search (dense + sparse), re-ranking, multi-vector retrieval. Optimizes latency and recall through fine-tuning embedding models and index tuning.

Rolle	Pflicht	Beschreibung
LLM Engineer		Defines embedding and vector DB strategy for the LLM platform. Establishes guidelines for embedding model selection, vector DB, index sharding, and retrieval quality monitoring.

Rolle	Pflicht	Beschreibung
LLM Engineer		Shapes enterprise embedding infrastructure strategy. Defines approaches to centralized embedding services, managing billions of vectors, cost optimization, and retrieval quality at scale.

Junior 1 Anforderungen

LLM Engineer

Knows text embeddings and vector database basics. Generates embeddings via sentence-transformers, stores and searches in ChromaDB. Understands cosine similarity and basic semantic search.

Middle 1 Anforderungen

LLM Engineer

Independently designs embedding pipelines: model selection (OpenAI, Cohere, BGE), chunking strategies, and metadata filtering. Configures Pinecone/Weaviate for production workloads with recall optimization.

Senior 1 Anforderungen

LLM Engineer

Designs scalable embedding infrastructure: hybrid search (dense + sparse), re-ranking, multi-vector retrieval. Optimizes latency and recall through fine-tuning embedding models and index tuning.

Lead / Staff 1 Anforderungen

LLM Engineer

Defines embedding and vector DB strategy for the LLM platform. Establishes guidelines for embedding model selection, vector DB, index sharding, and retrieval quality monitoring.

Principal 1 Anforderungen

LLM Engineer

Shapes enterprise embedding infrastructure strategy. Defines approaches to centralized embedding services, managing billions of vectors, cost optimization, and retrieval quality at scale.

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