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Ai Rag Jobs in Seattle, WA (NOW HIRING)

Saransh Inc

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$117K - $162K/yr

... augmented generation (RAG), multi-agent orchestration, and foundation model capabilities to ... AI Agent & LLM Engineering • Design and build multi-agent systems, orchestration layers, and ...

Yantran LLC

Software Engineer-AI

Bellevue, WA

... as RAG, fine-tuning, and prompt engineering Experience with agent/orchestration frameworks like Semantic Kernel, Microsoft Agent Framework, Azure AI Foundry, or equivalents (e.g., LangChain ...

Spar Information Systems

AI ML Engineer

Bellevue, WA · On-site +1

Develop and operationalize RAG (Retrieval-Augmented Generation) pipelines integrating LLMs (e.g ... Build AI-powered automation for privacy operations including intelligent DSR routing, threshold ...

Merican

Senior AI Engineer - Privacy

Bellevue, WA · On-site

$117K - $162K/yr

Develop and operationalize RAG (Retrieval-Augmented Generation) pipelines integrating LLMs (e.g ... Build AI-powered automation for privacy operations including intelligent DSR routing, threshold ...

Weyerhauser Co

AI Architect

Seattle, WA · On-site

$71.75 - $94.50/hr

Architect and implement the foundational platform for Agentic and classic AI, encompassing model orchestration, retrieval-augmented generation (RAG), memory systems, and Agent-to-Agent (A2A ...

ZS

Lead AI Engineer

Bellevue, WA · On-site

$155K - $167K/yr

What you'll do: Lead AI Engineer in the Platforms and Products will... We are seeking a highly ... Build and optimize RAG pipelines using embeddings, chunking strategies, and vector search.

Weyerhaeuser

AI Architect

Seattle, WA

$131K - $196K/yr

Architect and implement the foundational platform for Agentic and classic AI, encompassing model orchestration, retrieval-augmented generation (RAG), memory systems, and Agent-to-Agent (A2A ...

Stoke Space

Senior AI Software Engineer

Kent, WA · On-site

$154K - $231K/yr

Build and maintain RAG pipelines leveraging vector databases to enable intelligent search and retrieval * Develop comprehensive evaluation frameworks (evals) to measure, monitor, and improve AI ...

Weyerhaeuser Company

AI Architect

Seattle, WA

$71.75 - $94.50/hr

Architect and implement the foundational platform for Agentic and classic AI, encompassing model orchestration, retrieval-augmented generation (RAG), memory systems, and Agent-to-Agent (A2A ...

Stoke Space

Senior AI Software Engineer

Kent, WA · On-site +1

$154K - $231K/yr

Build and maintain RAG pipelines leveraging vector databases to enable intelligent search and retrieval * Develop comprehensive evaluation frameworks (evals) to measure, monitor, and improve AI ...

Chabez Tech LLC

Senior AI Consultant

Bellevue, WA · Hybrid

Frisco TX or Bellevue WA - Onsite Mandatory Areas:- AI,ML NLP LLM/SLM RAG About the Role We are looking for a Senior AI Consultant to serve as a strategic advisor and technical architect for our AI ...

Weyerhaeuser

Senior AI Architect

Seattle, WA

$135K - $203K/yr

Architect and implement the foundational platform for Agentic and classic AI, encompassing model orchestration, retrieval-augmented generation (RAG), memory systems, and Agent-to-Agent (A2A ...

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All JobsAi Rag JobsAi Rag Jobs in Seattle, WA

Ai Rag information

See Seattle, WA salary details

$36.4K

$66.3K

$95K

How much do ai rag jobs pay per year?

As of Jun 16, 2026, the average yearly pay for ai rag in Seattle, WA is $66,285.00, according to ZipRecruiter salary data. Most workers in this role earn between $55,800.00 and $74,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as an AI Researcher, and why are they important?

To thrive as an AI Researcher, you need a strong background in computer science, mathematics, and machine learning, usually with an advanced degree such as a Master's or Ph.D. Proficiency with programming languages like Python, deep learning frameworks (e.g., TensorFlow, PyTorch), and familiarity with scientific research tools is essential. Critical thinking, creativity, and effective collaboration are vital soft skills for generating novel ideas and working in multidisciplinary teams. These skills and qualities are crucial to drive innovation and solve complex problems in the rapidly evolving field of artificial intelligence.

What is the difference between Ai Rag vs Data Analyst?

AspectAi RagData Analyst
Required CredentialsTypically a diploma or certification in AI, machine learning, or related fieldsBachelor's degree in statistics, mathematics, or related fields
Work EnvironmentTech companies, AI startups, research labsBusiness, finance, healthcare, and various industries
Employer & Industry UsagePrimarily in AI development and researchAcross industries for data interpretation and decision-making
Common Search & ComparisonYesYes

Ai Rag and Data Analyst roles share overlapping skills in data handling and analysis, but Ai Rag focuses more on AI-specific applications and machine learning, while Data Analysts concentrate on interpreting data to inform business decisions. Both roles are vital in data-driven industries, with Ai Rag often working in AI development environments and Data Analysts supporting strategic insights across sectors.

What are AI RAGs?

AI RAGs, or Retrieval-Augmented Generation systems, are a type of artificial intelligence that combines the power of retrieving information from large databases or documents with generating human-like text responses. This approach allows AI models to provide more accurate, up-to-date, and contextually relevant answers by referencing external data sources during the generation process. RAGs are commonly used in applications like chatbots, search engines, and customer support systems, where comprehensive and factual responses are important.

What are some common challenges faced by AI RAG (Retrieval-Augmented Generation) engineers when integrating retrieval systems with large language models?

AI RAG engineers often encounter challenges such as ensuring seamless integration between retrieval systems and language models, maintaining low latency for real-time responses, and handling the quality and relevance of retrieved data. Additionally, tuning the system to balance retrieval accuracy with generative fluency can be complex, especially when dealing with large or unstructured datasets. Collaboration with data engineers, ML researchers, and product teams is essential to address these challenges and optimize system performance.
What job categories do people searching Ai Rag jobs in Seattle, WA look for? The top searched job categories for Ai Rag jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Ai Rag jobs? Cities near Seattle, WA with the most Ai Rag job openings:
Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Seattle, WA as of June 2026, with employment types broken down into 100% Full Time. Highlights an 65% Physical, 5% Hybrid, and 30% Remote job distribution, with an average salary of $66,285 per year, or $31.9 per hour.

Senior AI Engineer - Privacy

Saransh Inc

Bellevue, WA • On-site

$117K - $162K/yr

Contractor

Posted 13 days ago

Job description

Role: Senior AI Engineer – Privacy
Location: Bellevue, WA (Onsite from Day 1)
Job Type: Contract
 
Must Have Skills:
  • 7 yrs of exp – AI Engineer – Privacy
  • 7 yrs of exp – Azure Data Factory, Azure , GitLab
  • 5 yrs of exp – Databricks, Snowflake
Description:
  • The Senior AI Engineer – Privacy will design, build, and operationalize AI and agentic systems that power Client data privacy platform at scale.
  • Embedded within the Data & Intelligence organization's Privacy practice, this engineer will apply large language models (LLMs), retrieval-augmented generation (RAG), multi-agent orchestration, and foundation model capabilities to automate, enhance, and scale privacy operations — including Data Subject Request (DSR) processing, consent management, regulatory compliance monitoring, and privacy impact assessment workflows — across a customer base of over 100 million.

AI Agent & LLM Engineering
• Design and build multi-agent systems, orchestration layers, and agentic workflows using frameworks such as LangChain, LangGraph, Google ADK, or equivalent.
• Develop and operationalize RAG (Retrieval-Augmented Generation) pipelines integrating LLMs (e.g. Claude, Gemini, GPT-4) into production privacy applications.
• Implement structured prompting, decision workflows, and tool orchestration — including MCP (Model Context Protocol)-based architectures — for autonomous agent systems.
• Build AI-powered automation for privacy operations including intelligent DSR routing, threshold monitoring, agentic data quality checks, and automated regulatory notifications.
• Enable human-in-the-loop controls and escalation paths for AI-assisted decisions in sensitive privacy workflows.
Data & ML Engineering
• Build and optimize data pipelines using Azure Data Factory, Databricks, Snowflake, or PySpark to support AI model training, fine-tuning, and inference.
• Apply prompt engineering, few-shot learning, and fine-tuning techniques to adapt foundation models for privacy-specific use cases.
• Implement vector databases and embedding strategies to power RAG pipelines over Client internal privacy knowledge bases and policy documents.
• Ensure data quality, lineage, and governance standards are maintained across all AI training and inference pipelines.
Cloud & MLOps
• Deploy and manage AI workloads on Azure or AWS, including serverless inference endpoints, container registries, and GPU/compute resources.
• Build and maintain CI/CD pipelines for AI model deployment using GitLab or Azure DevOps, applying MLOps best practices.
• Implement monitoring, alerting, and performance tracking for production AI models and agent systems using Splunk, AppDynamics, or Grafana.
• Apply containerization (Docker) and orchestration (Kubernetes) to ensure scalable and reliable AI service deployments.
Responsible AI & Compliance
• Implement responsible AI principles — including fairness, transparency, and explainability — across all AI systems used in privacy operations.
• Ensure AI-assisted workflows comply with CCPA, CPRA, TCPA, and other applicable state and federal privacy regulations.
• Design and maintain audit trails and human-in-the-loop checkpoints for AI decisions affecting consumer privacy rights.
• Collaborate with legal, compliance, and privacy operations teams to translate regulatory requirements into AI solution guardrails and constraints.
Technical Leadership & Collaboration
• Partner with data engineers, full stack engineers, product managers, and privacy stakeholders to deliver end-to-end AI-powered privacy solutions.
• Mentor junior engineers on AI/ML engineering practices, agentic patterns, and responsible AI design principles.
• Produce clear technical documentation, architecture diagrams, and model cards for AI systems in production.
• Contribute to internal accelerators, reusable AI component libraries, and the broader engineering community of practice.