Retrieval-Augmented Generation: grounding LLM responses by retrieving relevant documents from a vector store or search index before generation, so answers cite real sources instead of hallucinating. Primary AI lifecycle stage: agents and application.
1,963 active AI roles across 200 companies in our index reference RAG as of today.
The companies with the most active RAG listings are: JPMorgan Chase (149 roles), Amazon (147 roles), Adobe (76 roles), Google (67 roles), Walmart (64 roles).
RAG primarily belongs to the agents and application stages of the AI lifecycle. In current hiring, RAG roles concentrate at: agents (84%), application (5%).
The sectors with the most active RAG hiring are: Enterprise, Big Tech, Banking.
Retrieval-Augmented Generation: grounding LLM responses by retrieving relevant documents from a vector store or search index before generation, so answers cite real sources instead of hallucinating.
Primary AI lifecycle stage: agents and application.
As of today, 1,963 active AI roles across 200 companies in our index reference RAG. Hiring concentrates at the agents (84%) and application (5%) stages. Most common sectors: Enterprise, Big Tech, Banking.
16 AI roles tagged rag.
| Company | Title | Sector | AI score | Other tags |
|---|---|---|---|---|
| Ford | Senior Data Scientist | Auto | 8 | Agent orchestration · Tool use · LLM observability · Fine-tuning · Inference infra · Model serving · Recommender systems |
| Ford | Sr. Software Engineer, AI Specialist | Auto | 8 | Agent orchestration · Semantic search · Vector DB · LLM observability · Evals · Model serving |
| Ford | Generative AI Engineer - Ford Pro Intelligence | Auto | 8 | Agent orchestration · Tool use · LLM observability · Guardrails · Model serving · Inference infra |
| Ford | AI Engineer | Auto | 8 | Agent orchestration · Tool use · Vector DB · Guardrails · LLM observability |
| Ford | Data Scientist | Auto | 8 | Agent orchestration · Evals · LLM observability |
| Ford | Full Stack Software Engineer, AI Integration | Auto | 8 | Agent orchestration · Tool use · Vector DB · Model serving · LLM observability · Evals |
| Ford | Applied AI/ML Software Engineer-Supply Chain AI and Decision Intelligence | Auto | 8 | Agent orchestration · LLM observability · Guardrails |
| Ford | AI Engineer | Auto | 7 | Model serving · Fine-tuning |
| Ford | Staff Engineer, Ford Pro Intelligence | Auto | 7 | Inference infra · Model serving · Vector DB · Fine-tuning · LLM observability |
| Ford | Applied Data Scientist | Auto | 7 | Vector DB |
| Ford | Software Engineer | Auto | 7 | Vision · Inference infra · Model serving · Agent orchestration |
| Ford | Systems Engineer – End-to-End Software Diagnostics & Observability | Auto | 7 | Agent orchestration · LLM observability · Inference infra · Model serving |
| Ford | Data Engineer- Full Stack | Auto | 7 | Vector DB · Agent orchestration · LLM observability · Guardrails |
| Ford | AI-Accelerated Full Stack Software Development Engineer | Auto | 5 | Vector DB · LLM observability · Model serving |
| Ford | Director, Data Engineering and Architecture | Auto | 5 | Vector DB · Agent orchestration |
| Ford | Cloud Solution Architect | Auto | 5 | Agent orchestration · LLM observability · Inference infra · Model serving |