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Rag Engineer Jobs (NOW HIRING)

Senior AI/LLM Engineer

Irving, TX · On-site

$100K - $137K/yr

Faithfulness Relevance NDCG MRR Trace-level RAG evaluation (Langfuse) Data Engineering & ETL Prefect 2.x / 3.x Flows, tasks, futures Deployments (YAML) Scheduling ETL/ELT design Schema evolution ...

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Showing results 1-20

Rag Engineer information

See salary details

$59.5K

$90.5K

$153.5K

How much do rag engineer jobs pay per year?

As of Jul 19, 2026, the average yearly pay for rag engineer in the United States is $90,511.00, according to ZipRecruiter salary data. Most workers in this role earn between $68,500.00 and $105,000.00 per year, depending on experience, location, and employer.

What does a RAG engineer do?

A RAG engineer specializes in managing and analyzing Red, Amber, and Green (RAG) status indicators to monitor project or system performance. They often work with data visualization tools and reporting systems to identify issues and support decision-making in technical or operational environments.

What is the difference between Rag Engineer vs Textile Technician?

AspectRag EngineerTextile Technician
Required CredentialsEngineering degree, technical certificationsDiploma or degree in textiles or related field
Work EnvironmentFactories, manufacturing plants, R&D labsTextile mills, production facilities, quality control labs
Industry UsageDesigning and improving rag production processesMonitoring textile quality, testing fabrics

While both roles involve working within the textile industry, a Rag Engineer primarily focuses on the engineering aspects of rag production, process optimization, and machinery, whereas a Textile Technician concentrates on fabric testing, quality control, and ensuring textile standards are met. The roles often overlap in industry settings but differ in technical focus and responsibilities.

Which 3 jobs will survive AI?

For a Rag Engineer, roles that require complex manual dexterity, problem-solving in unpredictable environments, or specialized craftsmanship are less likely to be automated by AI. These include skilled trades such as welding, electrical work, and mechanical repair, which depend on hands-on expertise and adaptability. Continuous learning and certification in specialized tools or techniques help ensure job security in evolving technological landscapes.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer or AI research director, often requiring advanced skills in programming, data analysis, and deep learning. These roles may involve leading projects, developing innovative algorithms, and working with large datasets, usually in a corporate or research environment. Compensation at this level reflects significant expertise, experience, and responsibility in the AI field.

What engineers make $500,000?

Senior engineers in specialized fields such as petroleum, aerospace, or software engineering can earn $500,000 or more annually, especially with experience, advanced skills, and leadership roles. High compensation often involves working in high-demand industries, holding advanced certifications, or taking on executive-level responsibilities.
More about Rag Engineer jobs
What cities are hiring for Rag Engineer jobs? Cities with the most Rag Engineer job openings:
What states have the most Rag Engineer jobs? States with the most job openings for Rag Engineer jobs include:
Infographic showing various Rag Engineer job openings in the United States as of July 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $90,511 per year, or $43.5 per hour.
GenAI / Agentic AI Engineer (RAG & LLM Apps

GenAI / Agentic AI Engineer (RAG & LLM Apps

ConglomerateIT

Atlanta, GA • On-site

Other

Posted 19 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.