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Rag Jobs (NOW HIRING)

LTS

RAG and Evaluation Engineer

OR · On-site +1

LTS is seeking a RAG & Evaluation Engineer to join a small, senior engineering team applying frontier AI to one of the most consequential legacy systems still running in production today. The mission ...

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 - $168K/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. • ...

LTS

RAG and Evaluation Engineer

LTS is seeking a RAG & Evaluation Engineer to join a small, senior engineering team applying frontier AI to one of the most consequential legacy systems still running in production today. The mission ...

BrightAI

Senior AI Engineer - LLM, RAG

Palo Alto, CA · On-site

$123K - $168K/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. • ...

BrightAI

Senior AI Engineer - LLM, RAG

Palo Alto, CA · On-site

$123K - $168K/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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Rag information

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$40.5K

$78.8K

$118.5K

How much do rag jobs pay per year?

As of Jul 11, 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 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, AI research director, or executive roles like AI CTO. These roles often require advanced skills in programming, data analysis, and experience with AI frameworks, and may involve leadership responsibilities or specialized expertise in areas like deep learning or natural language processing.

Is RAG in demand?

RAG (Retrieval-Augmented Generation) is an emerging technology in the AI and machine learning fields, increasingly used in applications like chatbots and data analysis. Demand for skills in RAG and related AI tools is growing as organizations seek advanced natural language processing solutions. Professionals with knowledge of AI models, data retrieval, and programming languages such as Python are increasingly sought after in this area.

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 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 is a RAG job?

A RAG job typically refers to a role involving Red, Amber, and Green (RAG) status reporting, often used in project management to indicate progress or risk levels. Such jobs may require skills in data analysis, reporting tools, and project coordination to monitor and communicate project health effectively.

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 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 jobs pay 500,000 a year in the US?

High-paying jobs that can reach or exceed $500,000 annually in the US include roles such as senior corporate executives, investment bankers, specialized surgeons, and successful entrepreneurs. These positions often require advanced degrees, extensive experience, and strong industry networks, with compensation frequently including bonuses, stock options, or profit sharing.
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 July 2026, with employment types broken down into 72% Full Time, 9% Part Time, 2% Temporary, and 17% Contract. Highlights an 85% In-person, and 15% Remote job distribution, with an average salary of $78,753 per year, or $37.9 per hour.
Senior AI Engineer - LLM, RAG

Senior AI Engineer - LLM, RAG

BrightAI Corporation

Palo Alto, CA • On-site

$122K - $168K/yr

Full-time

Re-posted 6 days ago

Job description

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 visual, spatial, and temporal data from billions of real-world events-captured across edge devices, mobile sensors, and cloud infrastructure-to enable intelligent decision-making at scale.
We are now hiring a Senior AI Engineer - LLM, RAG to lead the development of Retrieval-Augmented Generation (RAG) systems that harness the power of large language models (LLMs) and real-world knowledge sources. This role is pivotal to building next-generation intelligent assistants that help technicians and operators troubleshoot complex issues in industrial settings.
You'll work at the intersection of NLP, foundational models, and real-time information systems-developing intelligent tools that turn manuals, technician notes, and sensor data into actionable, conversational guidance for the physical world.
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.
  • Develop AI-powered assistants to support technicians in diagnosing and resolving anomalies or failures in factory, plant, or industrial settings.
  • Build pipelines to ingest, preprocess, and index large corpora of documents (manuals, logs, notes, procedures) for semantic search and grounding.
  • Customize and fine-tune foundational models to incorporate domain-specific language, tone, and logic for industrial troubleshooting scenarios.
  • Collaborate with product, data, and cloud teams to design scalable, privacy-compliant, and latency-sensitive LLM applications.
  • Design evaluation strategies to measure performance, accuracy, and user experience of 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 product roadmap.
Educational Background
  • M.S. or Ph.D. in Computer Science, AI, Machine Learning, or a related field, with specialization in NLP or deep learning.
  • Strong research or applied background in large language models (LLMs) and retrieval-augmented generation (RAG) systems. Agentic RAG experience is highly desirable.
Required Skills & Expertise
  • 5+ years of experience in machine learning or AI with a strong focus on NLP, LLMs, or conversational AI.
  • Fluency with modern LLMs and open-source foundational models (e.g., LLAMA, Falcon, Mistral, GPT, Claude).
  • Experience building RAG pipelines with tools like LangChain, LlamaIndex, or custom vector database integrations, with at least one production grade system was built.
  • Fluency with prompt engineering, instruction tuning, or fine-tuning open-source models.
  • Deep understanding of document retrieval (semantic search, embedding generation, similarity metrics) and vector stores (e.g., FAISS, Weaviate, Pinecone).
  • Strong foundation in core machine learning techniques, including experience with reinforcement learning (RL) or decision-making models.
  • Proficiency with ML development frameworks such as PyTorch, Hugging Face Transformers, or similar. Strong Python programming is a must.
  • Experience integrating AI systems into real-world applications with user-facing interfaces and operational constraints.
  • Excellent problem-solving and critical thinking skills; ability to design solutions for complex, ambiguous problems.
  • Strong written and verbal communication skills, with ability to collaborate cross-functionally with engineers, product managers, and domain experts.
Bonus Qualifications
  • Experience applying LLMs in industrial or physical infrastructure settings (e.g., manufacturing, logistics, utilities, energy).
  • Knowledge of industrial control systems, maintenance workflows, or technician support processes.
  • Exposure to multimodal models or integrating textual data with sensor and/or time-series data.
  • Prior experience in a startup or a fast-paced environment building LLM-powered products from the ground up.
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About BrightAI

Sourced by ZipRecruiter

Industry

Software development

Company size

11 - 50 Employees

Headquarters location

San Francisco, CA, US

Year founded

2019

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