RAG Engineer Jobs (2026)
RAG (Retrieval-Augmented Generation) Engineers build systems that combine large language models with external knowledge retrieval to produce accurate, grounded, and up-to-date responses. As enterprises deploy LLM applications, RAG has become the standard architecture for building reliable AI products.
Last updated: October 1, 2026
Latest RAG Engineer Jobs
View all jobsSenior AI Engineer - LLM RAG
Data Engineer (Part-Time) / Software Engineer & Data Engineer (Full-Time) (LOCAL FOR PARAGUAY)
AI/RAG engineer
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Senior AI Systems Architect (RAG & Agentic AI)- Contract Role
Senior AI/ML Engineer - LLM, RAG and Agentic AI
AI Engineer (RAG) - Remote Work
Junior AI Engineer (Research & Innovation) LOCAL FOR PARAGUAY
Senior AI/ML Engineer - Agentic AI & RAG
Machine Learning Manager, Feed Relevance (Retrieval)
Staff Machine Learning Engineer, Retrieval
Senior Engineer, AI Object Storage
Staff Engineer, AI Object Storage
Lead Evaluation Engineer – Scenario Coverage & Datasets (Autonomous Driving)
CX Knowledge Base & Conversational AI Manager
Staff Engineer, Distributed Storage and HPC & AI Infrastructure
Senior Staff Applied AI Engineer - Context Retrieval
Junior AI Application Engineer – AI Products (LLM & RAG) (m/f/d)
AI Application Engineer – AI Products (LLM & RAG) (m/f/d)
Senior Data Engineer - Storage & Messaging
Senior Software Engineer, AI Retrieval
Frequently Asked Questions
What does a RAG Engineer do?
RAG Engineers design and build retrieval-augmented generation pipelines that combine LLMs with vector databases, document processing, embedding models, and search systems. They optimize retrieval quality, chunk strategies, re-ranking, and end-to-end pipeline performance. RAG has become the standard enterprise architecture for deploying reliable LLM applications, making this one of the fastest-growing specializations as AI job openings have increased 25.2% year-over-year.
What skills do RAG engineers need?
Key skills include Python (required in 47-58% of AI job listings), vector databases (Pinecone, Weaviate, ChromaDB, Qdrant), embedding models, LLM APIs, LangChain or LlamaIndex, document parsing, and evaluation frameworks. Cloud platform experience with AWS, GCP, or Azure is increasingly essential for production deployments. Understanding of information retrieval, search relevance, and SQL (used by 50% of developers) rounds out the skill set for competitive candidates.
What is the salary for RAG engineers?
RAG engineer salaries align closely with LLM engineer compensation, ranging from $101K-$220K for mid-level roles and $220K-$350K+ for senior positions. AI Engineers working on RAG systems average $140K-$185K in base salary with total comp around $211K. Remote RAG engineering roles are widely available since AI jobs are 3x more likely to offer remote work than non-AI positions. Location premiums of 20-50% apply in SF, NYC, and Boston.
AI Job Insights for RAG Engineer Jobs
Salary Range (Yearly, USD)
$48K - $555K
Median $202K from 15 listings with salary data
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