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Ai Rag Jobs in Seattle, WA (NOW HIRING)

TEEMA

AI Specialist

Seattle, WA · Remote

$90 - $120/hr

Architect and implement retrieval-augmented generation (RAG) systems that ground AI responses in WelbeHealth's proprietary data, ensuring accuracy, relevance, and compliance with healthcare data ...

OfferUp

AI/ML Engineer

Bellevue, WA

$140K - $160K/yr

As an AI Engineer on the Data team, you will own the production systems - from agentic workflows and RAG pipelines to LLM integrations - that turn our marketplace intelligence into real customer ...

OfferUp

AI/ML Engineer

Bellevue, WA · On-site

$140K - $160K/yr

As an AI Engineer on the Data team, you will own the production systems - from agentic workflows and RAG pipelines to LLM integrations - that turn our marketplace intelligence into real customer ...

MSRCosmos

AI Solution Architect

Bellevue, WA · Hybrid

$71 - $93.75/hr

... in RAG architectures LLMs and a broad sound knowledge of Microsoft technologies NET Azure M365 ... AI solutions from ideation to implementation in enterprise environments Expertise in RAG ...

Programmers.io

Azure AI/ML Engineer

Bellevue, WA · On-site

$62 - $77/hr

... building RAG-based systems and implement RAG pipelines using embeddings| vector search| and LLMsSolid experience with Vector Databases and embedding-based search e.g. Azure AI SearchPractical ...

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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.
Generative AI Applications Engineer (Agents & RAG)

Generative AI Applications Engineer (Agents & RAG)

Accenture Federal Services

Seattle, WA

$64.75 - $86.25/hr

Other

Posted 15 days ago

Accenture Federal Services rating

8.4

Company rating: 8.4 out of 10

Based on 19 frontline employees who took The Breakroom Quiz

45th of 428 rated business services

Job description

Build AI that matters. We ship production GenAI apps for confidential federal programs across defense, national security, public safety, civilian, and military health where reliability, privacy, and safety aren't optional. AFS is a technology company within global Accenture and a Glassdoor Top 100 Best Place to Work. You'll join a collaborative, inclusive community with handson growth, certifications, and industry training. We ship in weeks, not quarters, and measure success with latency, reliability, safety, and cost. 

Confidentiality matters: We don't disclose program details publicly. If you advance, we'll share specifics during the process. 

Role Overview 

You'll turn mission needs into secure, reliable, and scalable GenAI applications no model training required. This is a hands-on role across agentic workflows, RAG, prompt/policy design, LLM evaluation, and platform integration. You'll own the end-to-end path from use case evaluation production deployment operational excellence, partnering with product, security, data, and SRE to ship features safely and at scale. 

What You'll Do (Day to Day) 

  • Design & ship mission grade GenAI: Build agentic workflows and RAG systems tailored to mission data and environments; target low hallucination, tight p95 latency, and predictable cost. 
  • Agent frameworks & orchestration: Apply patterns from LangChain/LlamaIndex/Semantic Kernel; design task decomposition, tool use, guardrails, and recovery/fallback strategies. 
  • Platform integration (no model training): Implement with AWS Bedrock, Azure OpenAI, Google Vertex AI, Amazon Kendra, and managed services (e.g., Document AI, Gemini, Gemma). 
  • LLM selection & evaluation: Compare models for quality, safety, latency, cost; author/test prompts & policies; deploy with observability and safe rollback/fallback. 
  • RAG done right: Build retrieval pipelines & vector search (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma); handle data prep, chunking, metadata, and IRstyle evals (e.g., NDCG) to maximize signal to noise. 
  • Production rigor: Instrument metrics/logs/traces; run A/B experiments; maintain incident playbooks; and implement safety & compliance guardrails. 
  • SRE & FinOps for AI: Define SLIs/SLOs (quality/latency/safety/cost), run on call and postmortems, reduce MTTR; meter usage and optimize token/spend. 
  • Reusable platform components: Ship SDKs, CI/CD templates, Terraform/IaC modules, evaluation harnesses that accelerate multiple mission team not one-off projects. 
  • Operate in real world constraints: Deliver into hybrid, restricted, or air gapped environments with Zero Trust principles and audit ready controls. 

You'll Thrive Here If you have 

  • End-to-end ownership of production systems: integration deployment observability incident response. 
  • Hands-on experience with LLMs, transformer based apps, and RAG in production. 
  • Strong Python 
  • Experience with vector search and retrieval (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma) and grounding AI in enterprise/mission data. 
  • U.S. Citizenship 

Nice to Have 

  • Integration with leading cloud AI services or on prem inference stacks  
  • Background in LLM evaluation, prompt authoring/testing, A/B experimentation, and LLM Ops. 
  • Responsible AI expertise (privacy, security, bias, transparency, human in the loop) and data governance. 
  • Experience implementing tool using agents for API integration and external data access. 
  • Containerization & orchestration (Docker, Kubernetes, VMware) and scripting/automation (Linux Bash, PowerShell). 
  • Prior work in regulated/secure environments (e.g., ATO, STIGs, Zero Trust) with fast shipping. 
  • Familiarity with NVIDIA AI Foundations, OpenAI ChatGPT, and AI assisted dev tools (Cursor, Windsurf, Claude). 
  • Contributions to internal frameworks or opensource; mentorship of engineers. 
  • Clear communication with engineers, PMs, and security/compliance stakeholders. 

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