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Rag Jobs in California (NOW HIRING)

About rag & bone From our origins in New York in 2002, rag & bone was founded on a belief of uncompromising ideals: a commitment to doing things the right way, not the easy way. To making things that ...

From our origins in New York in 2002, rag & bone was founded on a belief of uncompromising ideals: a commitment to doing things the right way, not the easy way. To making things that are as original ...

From our origins in New York in 2002, rag & bone was founded on a belief of uncompromising ideals: a commitment to doing things the right way, not the easy way. To making things that are as original ...

About rag & bone From our origins in New York in 2002, rag & bone was founded on a belief of uncompromising ideals: a commitment to doing things the right way, not the easy way. To making things that ...

From our origins in New York in 2002, rag & bone was founded on a belief of uncompromising ideals: a commitment to doing things the right way, not the easy way. To making things that are as original ...

From our origins in New York in 2002, rag & bone was founded on a belief of uncompromising ideals: a commitment to doing things the right way, not the easy way. To making things that are as original ...

From our origins in New York in 2002, rag & bone was founded on a belief of uncompromising ideals: a commitment to doing things the right way, not the easy way. To making things that are as original ...

Sales Specialist

Venice, CA · On-site

$20 - $22/hr

From our origins in New York in 2002, rag & bone was founded on a belief of uncompromising ideals: a commitment to doing things the right way, not the easy way. To making things that are as original ...

From our origins in New York in 2002, rag & bone was founded on a belief of uncompromising ideals: a commitment to doing things the right way, not the easy way. To making things that are as original ...

From our origins in New York in 2002, rag & bone was founded on a belief of uncompromising ideals: a commitment to doing things the right way, not the easy way. To making things that are as original ...

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Rag information

See California salary details

$40K

$77.7K

$116.9K

How much do rag jobs pay per year?

As of Aug 17, 2026, the average yearly pay for rag in California is $77,722.00, according to ZipRecruiter salary data. Most workers in this role earn between $60,200.00 and $92,300.00 per year, depending on experience, location, and employer.

What is a RAG?

RAG stands for Retrieval-Augmented Generation, a model architecture that combines information retrieval with generative AI. In this role, a RAG specialist or engineer works on designing, implementing, and optimizing systems that retrieve relevant data from large databases to provide more accurate and informed AI-generated responses. This position typically requires strong knowledge of natural language processing, information retrieval, and deep learning frameworks. RAG models are particularly useful in applications like customer support, search engines, and knowledge management systems.

What skills and qualifications are needed to thrive as a RAG engineer?

To thrive as a Retrieval-Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and software engineering, often with a degree in computer science or a related field. Familiarity with frameworks like PyTorch or TensorFlow, experience with vector databases, and knowledge of APIs for language models are typically required. Problem-solving, effective communication, and adaptability are crucial soft skills for collaborating with teams and navigating evolving technologies. These skills are important to successfully develop, deploy, and maintain RAG systems that enhance the performance and relevance of AI-driven applications.

What are common challenges faced by RAG engineers when integrating retrieval systems with large language models?

RAG engineers often encounter challenges in ensuring the seamless integration of retrieval systems with large language models, such as maintaining low latency while fetching relevant documents and ensuring retrieved data is contextually appropriate for generation tasks. Balancing retrieval accuracy and computational efficiency is key, especially when dealing with large-scale or real-time applications. Effective collaboration with data engineers, NLP researchers, and product teams is essential to continuously refine retrieval pipelines and improve the relevance of generated outputs.

What is the difference between Rag vs Data Analyst?

AspectRagData Analyst
Required CredentialsVaries, often no formal degreeBachelor's degree in data-related field, often certifications
Work EnvironmentFieldwork, on-site, or warehouse settingsOffice-based, computer-focused
Employer & Industry UsageConstruction, manufacturing, logisticsFinance, marketing, healthcare, tech
Common Search & ComparisonRag vs Data AnalystData Analyst roles and responsibilities

While Rags typically work in physical environments handling materials or equipment, Data Analysts focus on interpreting data to inform business decisions. Both roles require analytical skills but differ significantly in credentials, work setting, and industry applications.

What are the most commonly searched types of Rag jobs in California?

The most popular types of Rag jobs in California are:

What are popular job titles related to Rag jobs in California?

For Rag jobs in California, the most frequently searched job titles are:

What cities in California are hiring for Rag jobs?

Cities in California with the most Rag job openings:

Infographic showing various Rag job openings in California as of August 2026, with employment types broken down into 88% Full Time, 5% Part Time, 2% Temporary, and 5% Contract. Highlights an 75% Physical, 6% Hybrid, and 19% Remote job distribution, with an average salary of $77,722 per year, or $37.4 per hour.

GenAI Engineer (RAG Specialist)

K&K Global Talent Solutions Inc.

Mountain View, CA • On-site

Other

Re-posted 5 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.