Skill Profile

Embeddings & Vector DB

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

Role	Required	Description
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.

Role	Required	Description
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.

Role	Required	Description
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.

Role	Required	Description
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 requirements

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 requirements

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 requirements

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 requirements

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 requirements

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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