1

Retrieval Augmented Generation Jobs in California

AI/ML Engineer

Burbank, CA · On-site

$111K - $153K/yr

Build and deploy RAG (Retrieval-Augmented Generation) pipelines * Integrate LLMs via APIs (Azure OpenAI preferred) into enterprise applications * Develop and orchestrate agentic AI workflows with ...

This role focuses on building scalable systems leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), and agentic AI workflows . The ideal candidate will bring deep expertise ...

Senior Agentic AI Engineer

Long Beach, CA · On-site

$114K - $156K/yr

This role focuses on agentic workflows, retrieval-augmented generation (RAG), tool orchestration, evaluation, and production deployment of GenAI systems. You will work at the intersection of LLMs ...

Software Engineer (Java + GenAI)

San Jose, CA · On-site

$60.75 - $83.25/hr

... Retrieval-Augmented Generation (RAG) - Vector databases - Prompt engineering - Large Language Models (LLMs) - Application: Send suitable profiles and contact details to rams@vensoft.com

Senior Software Engineer, DevAI

San Diego, CA · On-site

$130K - $171K/yr

Advanced AI/ML: Strong expertise in Large Language Models (LLMs), including techniques like prompt engineering and Retrieval-Augmented Generation (RAG) * Coding Excellence: Proficiency in ...

Senior Agentic AI Engineer

Long Beach, CA · On-site +1

$87K - $189K/yr

This role focuses on agentic workflows, retrieval-augmented generation (RAG), tool orchestration, evaluation, and production deployment of GenAI systems. You will work at the intersection of LLMs ...

Data Engineer

Cupertino, CA · On-site

$141K - $169K/yr

... Retrieval Augmented Generation (RAG) techniques to enhance data analytics capabilities • Applying Machine Learning technologies for anomaly detection Minimum Qualifications Bachelor's degree in ...

Senior Staff Engineer, DevAI

San Diego, CA · On-site

$110K - $152K/yr

Advanced AI/ML: Strong expertise in Large Language Models (LLMs), including techniques like prompt engineering and Retrieval-Augmented Generation (RAG) * Coding Excellence: Proficiency in ...

Support advanced AI use cases, including LLM-based solutions, retrieval-augmented generation (RAG), and hybrid modeling approaches where appropriate * Collaborate with engineering teams to integrate ...

Data Scientist II

Irvine, CA · On-site +1

$82K - $127K/yr

Retrieval-Augmented Generation (RAG) * Feature engineering and model evaluation techniques * Experience working with cloud platforms such as Azure, AWS, or similar ecosystems * Familiarity with data ...

AI/ML technologies (e.g., LLMs, NLP, retrieval-augmented generation) - 6 Yrs of Exp * Jira, Azure DevOps, Confluence, and Smartsheet - 6 Yrs of Exp Must have Certifications: PMP, PMI-ACP, or ...

next page

Showing results 1-20

Retrieval Augmented Generation information

What are the typical daily responsibilities of a Retrieval Augmented Generation engineer?

A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.

What is a Retrieval Augmented Generation job?

A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.

What are the key skills and qualifications needed to thrive in the Retrieval Augmented Generation position, and why are they important?

To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.

What are the most commonly searched types of Retrieval Augmented Generation jobs in California? The most popular types of Retrieval Augmented Generation jobs in California are:
What cities in California are hiring for Retrieval Augmented Generation jobs? Cities in California with the most Retrieval Augmented Generation job openings:
Infographic showing various Retrieval Augmented Generation job openings in California as of July 2026, with employment types broken down into 88% Full Time, 9% Part Time, 1% Temporary, and 2% Contract. Highlights an 77% Physical, 3% Hybrid, and 20% Remote job distribution.

GenAI Engineer (RAG Specialist)

K&K Global Talent Solutions Inc.

Mountain View, CA • On-site

Other

Re-posted 6 days ago


Job description

Role Summary:


Focuses on implementing retrieval-augmented generation (RAG) pipelines, integrating LLMs with structured/unstructured data sources, and fine-tuning models for specific use cases.

Key Skills:

  • LangChain, LlamaIndex (formerly GPT Index), RAG architectures
  • OpenAI, HuggingFace models, Azure OpenAI Service
  • Prompt engineering, embeddings (e.g., FAISS, Pinecone)
  • Fine-tuning and model adaptation for domain-specific datasets
  • Python, RESTful APIs, orchestration frameworks.