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.
2,037 active AI roles across 199 companies in our index reference RAG as of today.
The companies with the most active RAG listings are: Amazon (167 roles), JPMorgan Chase (146 roles), Google (78 roles), Adobe (73 roles), Capital One (69 roles).
RAG primarily belongs to the agents and application stages of the AI lifecycle. In current hiring, RAG roles concentrate at: agents (85%), application (4%).
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, 2,037 active AI roles across 199 companies in our index reference RAG. Hiring concentrates at the agents (85%) and application (4%) stages. Most common sectors: Enterprise, Big Tech, Banking.
3504 AI roles tagged rag.
| Company | Title | Sector | AI score | Other tags |
|---|---|---|---|---|
| Amazon | Applied Scientist, AGI , AGI Information | Big Tech | 8 | Agent memory |
| NVIDIA | Software Engineering Intern, Automation Infra - 2026 | Semiconductors | 8 | Agent orchestration · Tool use · LLM observability |
| Warner Bros Discovery | Sr. Staff, Data Science & Applied AI | Media | 8 | Agent orchestration · Tool use · Guardrails · LLM observability · Vector DB · Agent research |
| Adobe | Machine Learning Architect 5 - GenAI Experiences | Enterprise | 8 | Agent orchestration · LLM observability · Recommender systems · Inference infra · Model serving |
| Smartsheet | Senior Manager, Engineering - AI & Automation | Seattle | 8 | Agent orchestration · LLM observability · Inference infra · Model serving |
| Robinhood | Staff Product Manager, Cortex | Fintech | 8 | Agent orchestration · Evals · Guardrails · LLM observability |
| Amazon | Applied Scientist , Amazon Customer Service | Big Tech | 8 | Agent orchestration · Fine-tuning · Evals · LLM observability |
| Amazon | Data Scientist, AWS Quick Data | Big Tech | 8 | Evals · Synthetic data · LLM observability |
| Amazon | Sr. Software Development Manager, MHLS Tech | Big Tech | 8 | Agent orchestration · LLM observability · Model serving · Guardrails · Multimodal |
| AT&T | Principal AI - Software Engineer | Telecom | 8 | Agent orchestration · Vector DB · LLM observability · Evals |
| NVIDIA | SOC AI Application Engineer — AI Services, Agents and Knowledge Systems | Semiconductors | 8 | Agent orchestration · Tool use · Vector DB · LLM observability · Model serving · Code gen |
| Workday | Senior/Principal Machine Learning Engineer | Enterprise | 8 | Agent orchestration · LLM observability · Model serving · Evals |
| Workday | Machine Learning Engineer III / Senior Machine Learning Engineer - AI Platform | Enterprise | 8 | Agent orchestration · Tool use · LLM observability · Evals · Model serving |
| F5 | Principal AI Engineer | Enterprise | 8 | Agent orchestration · Tool use · Agent research · LLM observability · Guardrails · Model serving |
| F5 | Principle AI Engineer | Enterprise | 8 | Agent orchestration · Tool use · Agent research · LLM observability · Guardrails · Vector DB · Model serving · Inference infra |
| GEICO | Sr. Software Engineer - Applied AI | Insurance | 8 | Agent orchestration · Tool use · Vector DB · LLM observability |
| F5 | Principal AI Engineer | Enterprise | 8 | Agent orchestration · Tool use · Agent research · LLM observability · Guardrails · Model serving · Inference infra |
| Databricks | Staff Software Engineer - AI Platform (NYC) | Data AI | 8 | Agent orchestration · LLM observability · Evals |
| Databricks | Senior Software Engineer - AI Platform (NYC) | Data AI | 8 | Agent orchestration · Evals · LLM observability · Guardrails |
| Stripe | Machine Learning Engineer, Support Experience | Fintech | 8 | Agent orchestration · Tool use · Evals · Fine-tuning · LLM observability |
| JPMorgan Chase | Data Scientist Lead - Vice President | Banking | 8 | Agent orchestration · Fine-tuning · Model serving · LLM observability |
| Freshworks | Lead - Data Scientist | Enterprise | 8 | Agent orchestration · LLM observability · Fine-tuning · Model serving |
| JPMorgan Chase | Applied AI & ML Lead – Markets Operations | Banking | 8 | Model serving · Agent orchestration |
| AI Engineer, Google Cloud Consulting (English, French) | Big Tech | 8 | Model serving · Vector DB · Fine-tuning | |
| Databricks | Senior Specialist Solutions Architect - AI & ML Engineer | Data AI | 8 | Agent orchestration · Tool use · Guardrails · Vector DB · Evals · LLM observability · Model serving · Inference infra |
| Amazon | Sr. Applied AI Solutions Architect - Public Sector, Amazon Connect | Big Tech | 8 | Agent orchestration · Tool use · LLM observability · Guardrails |
| Workday | Principal Machine Learning Engineer - Evisort AI | Enterprise | 8 | LLM observability · Model serving · Fine-tuning |
| CrowdStrike | Sr. Software Engineer (GenAI Platform) (Hybrid, ROU) | Enterprise | 8 | Agent orchestration · Model serving · Inference infra |
| Adobe | Sr. AI Systems Engineer- Agentic and Productivity Systems | Enterprise | 8 | Agent orchestration · Inference infra · Model serving · LLM observability · Evals · Vector DB |
| Microsoft | Principal Research Software Engineer | Big Tech | 8 | Agent orchestration · Tool use · LLM observability · Fine-tuning · Model serving · Code gen |