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Rag Llm Jobs (NOW HIRING)

Northern Base

Senior Solutions Architect

Chicago, IL · On-site

$142K - $150K/yr

RAG, LLM integration, Databricks Genie, CI/CD, IaC, HIPAA/SOC 2 Roles & Responsibilities * Design end-to-end architectures across front-end, backend, data platforms, APIs, and Azure cloud * Build ...

BrightAI

Senior AI Engineer - LLM, RAG

Palo Alto, CA · On-site

$123K - $168K/yr

... RAG-enabled systems in production settings. • Stay up to date with the latest advances in LLM architectures, retrieval methods, and prompt engineering, and integrate emerging techniques into the ...

AMSYS Talent

Sr Gen AI Engineer

Houston, TX · On-site

$99K - $137K/yr

Senior Generative AI Engineer (Azure / RAG / LLM) We're looking for a hands-on Senior AI Engineer to build and deploy production-grade generative AI solutions. This role focuses on taking use cases ...

BrightAI

Senior AI Engineer - LLM, RAG

Palo Alto, CA · On-site

$123K - $168K/yr

... RAG-enabled systems in production settings. • Stay up to date with the latest advances in LLM architectures, retrieval methods, and prompt engineering, and integrate emerging techniques into the ...

BrightAI

Senior AI Engineer - LLM, RAG

Palo Alto, CA · On-site

$123K - $168K/yr

... RAG-enabled systems in production settings. • Stay up to date with the latest advances in LLM architectures, retrieval methods, and prompt engineering, and integrate emerging techniques into the ...

CYNET SYSTEMS

ML Engineer II

Aliso Viejo, CA · On-site

$52 - $57/hr

Pay Range: $52/hr - $57/hr Requirement/Must Have: * 10+ years of experience in end-to-end RAG architecture ownership (ingestion → retrieval → generation) & LLM orchestration. * 8+ years of strong ...

JS Consulting

Data Scientist

Washington, DC · On-site

Experience with LoRA, LangChain, RAG, LLM Fine Tuning and PEFT, Knowledge Graphs. * Strong skills in developing GraphRAG, Chain of Thought (CoT), Tree of Thought (ToT), Reinforcement learning and AI ...

Precision Technologies Corp

AI Engineer

Dallas, TX · On-site

Contract Key Skills - AI, Python, Rag, LLM Overview We are seeking an AI Engineer with proven experience in building and scaling AI-powered applications . This role combines hands-on development with ...

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All JobsRag Llm Jobs

Rag Llm information

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$45K

$75.3K

$110K

How much do rag llm jobs pay per year?

As of Aug 4, 2026, the average yearly pay for rag llm in the United States is $75,300.00, according to ZipRecruiter salary data. Most workers in this role earn between $62,000.00 and $87,000.00 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 the United States as of July 2026, with employment types broken down into 97% Full Time, 1% Part Time, and 2% Contract. Highlights an 80% Physical, 5% Hybrid, and 15% Remote job distribution, with an average salary of $75,300 per year, or $36.2 per hour.

GenAI / Agentic AI Engineer (RAG & LLM Apps

ConglomerateIT

Atlanta, GA • On-site

Other

Re-posted 4 days ago

Job description

Title:GenAI / Agentic AI Engineer (RAG & LLM Apps)
Location: US - Remote / Hybrid (multiple locations)
Type: Full-time or Contract (W2/C2C)
Level: Mid Senior (10+ Years Only)

We are hiring RAG-first GenAI engineers to build LLM-powered applications and agentic workflows for enterprise clients. This role centers on retrieval quality and reliable LLM integration as the foundation for agentic features.

What you'll do

  • Architect end-to-end RAG: chunking, embedding selection, hybrid/semantic search, re-ranking, citation and evaluation

  • Build LLM-powered microservices and APIs (FastAPI / REST)

  • Integrate and orchestrate LLMs (OpenAI, Claude, Gemini, Llama) into product and internal workflows

  • Add agentic behavior on top of RAG: tool-calling, multi-step task execution, guardrails, hallucination handling

  • Stand up and tune vector-store infrastructure; deploy and monitor in production

Must have

  • Strong Python

  • End-to-end RAG experience

  • Vector databases (Pinecone, Weaviate, pgvector, FAISS, or similar)

  • LLM API integration (OpenAI / Anthropic / Gemini / Llama)

  • LangChain and/or LlamaIndex

  • FastAPI / REST and one cloud (AWS, Azure, or Google Cloud Platform)

Nice to have

  • LangGraph, MCP, DSPy

  • Knowledge graphs / Neo4j, evals/LangSmith, MLOps

Founded in 2014, is a global leader in delivering innovative IT solutions and services. Headquartered in the USA with a presence in the UK, Canada, and India, we specialize in offering industry-leading expertise and cutting-edge products that help our clients maximize their technological investments. Our focus on best-in-class solutions, a highly knowledgeable team, and proactive talent mapping ensure we remain at the forefront of the IT industry.

ConglomerateIT is driven by our Center for Excellence and Innovation, an initiative dedicated to keeping us ahead in a rapidly evolving technology landscape. We understand that building strong relationships is key to our success, and this commitment has enabled us to partner with Fortune 500 companies and leading system integrators worldwide. Our ability to provide local talent on a global scale ensures that we can meet the contingent project requirements of our clients efficiently and effectively.

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