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Rag Jobs (NOW HIRING)

BrightAI Corporation

Senior AI Engineer - LLM, RAG

Palo Alto, CA · On-site

$122.80K - $168.70K/yr

Senior AI Engineer - RAG Systems Bright.AI is a high-growth Physical AI company transforming how businesses interact with the physical world through intelligent automation. Our AI platform processes ...

Rag & Bone

Retail Sales Associate

Livermore, CA · On-site

$16.75 - $19.25/hr

SALES SPECIALIST Rag & Bone Sales Specialist rag & bone is looking for a sales representative to join our team in our Livermore Outlet location. This person will actively seek out and engage ...

Rag & Bone

PT sales specialist

Wrentham, MA · On-site

$16 - $18/hr

Rag and Bone is looking for a sales representative to join our team in our Wrentham office. This person will actively seek out and engage prospective customers to sell our product and/or services.

Rag & Bone

Sales Specialist

Marysville, WA · On-site

rag & bone is looking for a sales representative to join our team in our Seattle Premium Outlet location. This person will actively seek out and engage prospective customers to sell our product and ...

BrightAI

Senior AI Engineer - LLM, RAG

Palo Alto, CA · On-site

$123K - $168.90K/yr

Responsibilities : • Lead the architecture and development of RAG systems that combine LLMs (e.g., LLAMA, Mistral, Claude, GPT) with structured and unstructured external information sources. • ...

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$40.5K

$78.8K

$118.5K

How much do rag jobs pay per year?

As of May 30, 2026, the average yearly pay for rag in the United States is $78,753.00, according to ZipRecruiter salary data. Most workers in this role earn between $61,000.00 and $93,500.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a RAG Engineer, and why are they important?

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 some common challenges faced by RAG (Retrieval-Augmented Generation) 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 are RAGs in the context of AI and machine learning jobs?

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 job makes $10,000 a month without a degree?

High-paying jobs that can reach $10,000 a month without a degree include roles such as real estate brokers, sales managers, and certain skilled trades like electricians or plumbers with experience. Success in these fields often depends on skills, certifications, and performance rather than formal education, and they may require long hours or entrepreneurial effort.

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.

More about Rag jobs
What cities are hiring for Rag jobs? Cities with the most Rag job openings:
What are the most commonly searched types of Rag jobs? The most popular types of Rag jobs are:
What states have the most Rag jobs? States with the most job openings for Rag jobs include:
What job categories do people searching Rag jobs look for? The top searched job categories for Rag jobs are:
Rag jobs near you
Infographic showing various Rag job openings in the United States as of May 2026, with employment types broken down into 92% Full Time, 3% Part Time, and 5% Contract. Highlights an 78% Physical, 4% Hybrid, and 18% Remote job distribution, with an average salary of $78,753 per year, or $37.9 per hour.

LLM / RAG Evaluation Engineer

Prophecy Technologies

Austin, TX • On-site

Full-time

Posted 27 days ago

Job description

Job Summary
We are seeking an experienced LLM / RAG Evaluation Engineer to design, implement, and scale evaluation frameworks for Large Language Models (LLMs), Retrieval-Augmented Generation (RAG) systems, and agentic AI workflows. This role focuses on assessing quality, reliability, safety, robustness, and performance of production-grade Generative AI systems used in real-world applications.
Key Responsibilities
  • Design and execute LLM response evaluation pipelines, including automated and human-in-the-loop approaches
  • Evaluate RAG systems for retrieval accuracy, grounding, relevance, and hallucination detection
  • Build and apply evaluation metrics for agentic AI systems, including:
  • Multi-step reasoning
  • Tool usage
  • Planning and memory workflows
  • Develop Python-based evaluation frameworks, benchmarks, and testing utilities
  • Analyze model outputs, identify failure modes, and provide actionable insights to improve system performance
  • Define and track KPIs for Generative AI systems, covering quality, safety, robustness, and trustworthiness
  • Collaborate with ML engineers, researchers, and product teams to improve GenAI architectures
  • Validate and compare prompt strategies, retrieval strategies, and system designs
  • Clearly document evaluation methodologies, results, and recommendations for stakeholders

Required Skills & Experience
  • Strong proficiency in Python
  • Proven experience in LLM response evaluation (quality, coherence, accuracy, bias, hallucinations)
  • Hands-on experience with RAG systems and retrieval-based architectures
  • Understanding of agentic AI systems and multi-step reasoning workflows
  • Experience evaluating Generative AI systems in real or near-production environments
  • Knowledge of NLP fundamentals and LLM behavior
  • Experience with prompt engineering, prompt testing, and prompt evaluation

Preferred Skills
  • Experience with LLM orchestration frameworks (LangChain, LlamaIndex, etc.)
  • Familiarity with automated evaluation tools, benchmarks, and scoring frameworks
  • Experience designing or managing human evaluation workflows
  • Understanding of AI safety, reliability, bias, and trustworthiness principles
  • Prior experience evaluating production-grade GenAI systems

Nice to Have
  • Experience with vector databases and retrieval pipelines
  • Exposure to cloud-based AI platforms
  • Research or experimentation background in LLM evaluation and benchmarking

Apply