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Rag Llm Jobs in Riverside, CA (NOW HIRING)

Hyundai Capital

Sr. GenAI Architect

Irvine, CA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

HOW YOU WILL MAKE AN IMPACT 1. Define reference architecture (LLM selection, RAG, agent framework, APIs). Lead development of agentic and orchestrated multi-agent architectures. Establish coding ...

acralending

AI Engineer

Irvine, CA

LLM & AI Engineering * Implement Retrieval-Augmented Generation (RAG) systems using lending guidelines, overlays, investor matrices, and SOPs. * Build prompt orchestration, memory systems, and agent ...

acralending

AI Engineer

Irvine, CA

LLM & AI Engineering * Implement Retrieval-Augmented Generation (RAG) systems using lending guidelines, overlays, investor matrices, and SOPs. * Build prompt orchestration, memory systems, and agent ...

Hyundai Capital

Sr. GenAI Architect

Irvine, CA · On-site

$132K - $204K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

HOW YOU WILL MAKE AN IMPACT 1. Define reference architecture (LLM selection, RAG, agent framework, APIs). Lead development of agentic and orchestrated multi-agent architectures. Establish coding ...

Hyundai Capital America

Sr. GenAI Architect

Irvine, CA · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

HOW YOU WILL MAKE AN IMPACT 1. Define reference architecture (LLM selection, RAG, agent framework, APIs). Lead development of agentic and orchestrated multi-agent architectures. Establish coding ...

Acra Lending

AI Engineer

Irvine, CA · On-site

... RAG) systems using lending guidelines, overlays, investor matrices, and SOPs. • Build prompt ... Python, SQL, JavaScript/TypeScript • AI/LLM: OpenAI, Anthropic, open-source LLMs, Hugging Face ...

Acra Lending

AI Engineer

Irvine, CA · On-site

$140K - $160K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

LLM & AI Engineering * Implement Retrieval-Augmented Generation (RAG) systems using lending guidelines, overlays, investor matrices, and SOPs. * Build prompt orchestration, memory systems, and agent ...

Luxoft

Technical Lead

Irvine, CA · On-site

... LLM orchestration, multi-agent systems, and autonomous coding workflows * Oversee the development of enterprise Retrieval-Augmented Generation (RAG) pipelines, semantic chunking strategies, and ...

Segment (Twilio)

Lead Data Scientist (Agentic Solutions)

Irvine, CA · On-site

$171.10 - $213.90/hr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... RAG systems, or agentic workflows for business applications. Skills & Expertise * Expertise with modern AI orchestration frameworks (LangChain, LlamaIndex, Haystack) and deep understanding of LLM ...

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Showing results 21-40

All JobsRag Llm JobsRag Llm Jobs in Riverside, CA

Rag Llm information

See Riverside, CA salary details

$46.9K

$78.6K

$114.8K

How much do rag llm jobs pay per year?

As of Aug 15, 2026, the average yearly pay for rag llm in Riverside, CA is $78,558.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,700.00 and $90,800.00 per year, depending on experience, location, and employer.

What is the difference between Rag Llm vs Data Scientist?

AspectRag LlmData Scientist
Required CredentialsTypically a master's or PhD in AI, machine learning, or related fieldsUsually a master's or PhD in data science, statistics, or computer science
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, tech firms, consulting
Industry UsageAI research, natural language processing, machine learning projectsData analysis, predictive modeling, data-driven decision making

Rag Llm and Data Scientist roles often overlap in AI and data analysis fields, but Rag Llm focuses more on language models and AI research, while Data Scientists handle broader data analysis and modeling tasks. Both require advanced degrees and work in tech-driven environments, but their core responsibilities differ in scope and application.

What is a RAG LLM?

RAG LLMs, or Retrieval-Augmented Generation Large Language Models, are advanced AI systems that combine the strengths of traditional language models with external data retrieval systems. They work by first searching a relevant database or knowledge base for up-to-date information, and then using a language model to generate responses based on both the retrieved content and their own training. This approach helps LLMs provide more accurate, current, and contextually relevant answers, especially for specialized or rapidly changing topics. RAG LLMs are widely used in customer support, research, and enterprise applications to improve information accuracy and reliability.

How do RAG LLM engineers collaborate with data scientists and product teams to improve retrieval-augmented generation systems?

RAG LLM engineers often work closely with data scientists to fine-tune retrieval mechanisms, optimize model performance, and evaluate system outputs. They also collaborate with product teams to understand user needs, integrate feedback, and ensure the system delivers relevant, accurate information. Regular cross-functional meetings and code reviews are common, fostering a collaborative environment focused on continuous improvement and innovation in response to real-world challenges.

What are the key skills and qualifications needed to thrive as a Retrieval-Augmented Generation (RAG) LLM engineer?

To thrive as a Retrieval-Augmented Generation (RAG) LLM Engineer, you need a strong background in natural language processing, machine learning, and software development, often supported by a degree in computer science or a related field. Familiarity with frameworks like PyTorch, Hugging Face Transformers, vector databases, and cloud platforms, along with experience deploying large language models, is essential. Analytical thinking, problem-solving abilities, and effective communication are crucial soft skills for collaboration and innovation in this fast-evolving space. These skills ensure the development of robust, scalable, and accurate retrieval-augmented AI systems that meet real-world information needs.

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Rag Llm jobs near you
Infographic showing various Rag Llm job openings in Riverside, CA as of August 2026, with employment types broken down into 91% Full Time, 4% Part Time, and 5% Contract. Highlights an 76% Physical, 5% Hybrid, and 19% Remote job distribution, with an average salary of $78,558 per year, or $37.8 per hour.

Software Engineer - AI Engineer II/III

Applied Medical

Rancho Santa Margarita, CA • On-site

$80K/yr

Full-time

Medical, Life, Retirement, PTO

Re-posted 15 days ago

Applied Medical rating

8.0

Company rating: 8.0 out of 10

Based on 23 frontline employees who took The Breakroom Quiz

Job description

Applied Medical is a new generation medical device company with a proven business model and commitment to innovation fueled by rapid business growth and expansion. Our company has been developing and manufacturing advanced surgical technologies for over 35 years and has earned a strong reputation for excellence in the healthcare field. Our unique business model, combined with our dedication to delivering the highest quality products, enables team members to contribute in a larger capacity than is possible in typical positions.
Position Description
We are seeking a highly skilled AI Engineer with deep expertise in Large Language Models (LLMs), AI agent frameworks, and Model Context Protocols (MCP). The ideal candidate will design, develop, and deploy advanced AI-powered applications that leverage LLMs for reasoning, planning, and autonomous decision-making. In this role, you will build agentic systems capable of performing multi-step tasks, interacting with external systems, integrating with APIs and tools, and orchestrating complex workflows end-to-end. You will also implement and optimize MCP-based architectures to ensure reliable tool usage, persistent context management, and robust coordination between agents and backend services.
Collaboration is a fundamental part of our organization's culture and is essential to our continued success. As such, the successful candidate for this position is expected to work on-site, enabling them to engage fully with colleagues and contribute to cross-functional initiatives. Therefore, the ability to work collaboratively and contribute to a positive and supportive team environment is a key requirement for this role.
Key Responsibilities:
  • LLM Application Development: Design, fine-tune, and deploy LLM-based applications for reasoning, planning, retrieval, and automation.
  • Agent Systems: Build autonomous AI agents that interact with APIs, databases, and tools using frameworks like LangChain, LangGraph, AutoGen, or custom solutions.
  • Context & Memory Management: Implement Model Context Protocols (MCP) for persistent context, robust coordination between agents, and seamless tool usage.
  • Data & RAG Pipelines: Develop RAG pipelines, vector databases, and prompt-engineering strategies to enable context-aware responses.
  • Backend & Infrastructure: Build scalable backend systems and deploy solutions on cloud or on-prem environments (Azure, AWS), with containerization (Docker, Kubernetes).
  • Research & Innovation: Stay up-to-date with emerging LLM and agentic technologies and evaluate them for practical applications.
  • Collaboration & Governance: Work closely with data science, product, and engineering teams while ensuring responsible AI practices, model safety, and compliance with data privacy regulations.

Position Requirements
This position requires the following skills and attributes:
  • Bachelor's or Master's in Computer Science, AI/ML, or related field (Ph.D. preferred).
  • 2+ years building and deploying AI applications with LLMs (OpenAI, Anthropic, HuggingFace).
  • Strong Python skills, with experience in Git, FastAPI/Flask, and ML frameworks (PyTorch, TensorFlow).
  • Hands-on experience with agent frameworks (AutoGen, LangGraph, CrewAI) and orchestration strategies.
  • Proficiency in vector databases (Pinecone, Milvus, ChromaDB, FAISS) and embedding techniques.
  • Solid understanding of prompt engineering, RAG, and LLM optimization.
  • Familiarity with cloud/on-prem deployment and containerization (Docker, Kubernetes).
  • Experience implementing Model Context Protocols for context management and interoperability.
  • Experience with multi-agent systems, planning algorithms, or reinforcement learning with LLMs.
  • Knowledge of production-grade AI monitoring, logging, and observability.
  • Excellent problem-solving skills and ability to thrive in fast-paced, collaborative environments.

Benefits
  • Competitive compensation range: $80000 - $140000 / year (California).
  • Comprehensive benefits package.
  • Training and mentorship opportunities.
  • On-campus wellness activities.
  • Education reimbursement program.
  • 401(k) program with discretionary employer match.
  • Generous vacation accrual and paid holiday schedule.

Please note that the compensation range may be adjusted in the future, and bonus and incentive compensation plans may apply.
Our total reward package reflects our commitment to employee growth and well-being, as we invest in your development and offer a range of benefits designed to enhance your career and life.
All compensation and benefits are subject to plan documents and written agreements.
Equal Opportunity Employer
Applied Medical is an Equal Opportunity Employer. All qualified applicants will receive consideration for employment without regard to age, ancestry, color, disability (mental and physical), exercising the right to family care and medical leave, gender, gender expression, gender identity, genetic information, marital status, medical condition, military or veteran status, national origin, political affiliation, race, religious creed, sex (including pregnancy, childbirth, breastfeeding and related medical conditions), or sexual orientation, or any other status protected by federal, state or local laws in the locations where Applied Medical operates.

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