What does RAG mean in AI jobs?

RAG means retrieval-augmented generation. In jobs, it usually refers to systems that retrieve trusted context before asking a language model to generate an answer.

What skills do RAG engineers need?

Useful skills include Python or TypeScript, embeddings, search, vector databases, data pipelines, evaluation, prompt design, information architecture, and product judgment.

Are RAG engineer jobs different from LLM engineer jobs?

RAG engineering is often a specialization within LLM engineering. It focuses more deeply on retrieval quality, source data, grounding, and answer evaluation.

Data & AI job board

RAG Engineer Jobs

Find RAG engineer jobs and understand the retrieval, data, evaluation, and product skills behind production LLM applications.

Jobs

Data & AI opportunities

Featured roles

No results match these filters

Remove a filter or try different keywords.

Discover hidden Jobs

Access exclusive job openings unlisted on LinkedIn or mainstream job boards.

Be the First To Know

Receive fresh job alerts daily, ensuring you're always first in line.

Jobs from All Over the Internet

Leverage our advanced tech that aggregates the latest job offerings from every corner of the web.

More jobs available.
Upgrade to Premium to unlock job alerts and on-site applications.

Unlock all jobs

Loved by 5,000+ Data & AI professionals

AI and big data top the list as the fastest-growing skills

World Economic Forum — Future of Jobs Report 2025

RAG engineer jobs focus on retrieval-augmented generation: connecting language models to trusted context so answers are grounded in company documents, product data, policies, tickets, research, or customer knowledge bases.

What RAG engineers build

A RAG engineer usually works across ingestion, chunking, embeddings, vector search, reranking, prompt context, evaluation, and observability. The job is less about adding a model to an app and more about making the model use the right evidence at the right time.

Signals of a strong RAG job description

Clear ownership of retrieval quality, source freshness, and answer evaluation.
Work with data pipelines, documents, vector databases, search, or knowledge graphs.
Partnership with product, support, security, and subject matter experts.
Attention to privacy, permissioning, source attribution, and auditability.

Compare RAG with nearby AI roles

RAG work sits inside the broader LLM engineer jobs and GenAI jobs cluster. It also pairs naturally with LLMOps jobs once systems need monitoring, rollback, and cost controls.

For adjacent career paths, compare AI engineer jobs, machine learning engineer jobs, and the AI engineer vs machine learning engineer guide.

For pay and role scope, read LLM engineer salary and LLM engineer vs data scientist.

Frequently asked questions

Trusted by 5,000+ Data & AI professionals

1/4
The ratio of hired Data Analysts is expected to grow by 25% from 2020 to 2030 (Bureau of Labor Statistics).: #1
Data Analyst is and will be one of the most in-demand jobs for the decade to come.: 16%
16% of all US jobs will be replaced by AI and Machine Learning by 2030 (Forrester).