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Ai Rag Jobs in Seattle, WA (NOW HIRING)

Tata Consultancy Services

Gen AI Developer

Seattle, WA · On-site

$57.25 - $78.75/hr

They are seeking a Gen AI Developer to develop and implement generative AI models, optimize RAG pipelines, and work on various NLP tasks. Responsibilities : • Develop and implement generative AI ...

Connexions Data Inc

Gen AI Developer

Seattle, WA · On-site

$57.25 - $78.75/hr

Proficiency in RAG (Retrieval-Augmented Generation) techniques. Strong understanding of natural ... Work on NLP tasks such as text classification, summarization, and conversational AI. Perform data ...

Qaurs Techno Systems LLC

Senior AI Engineer

Bellevue, WA · On-site

$75K - $85K/yr

Develop Retrieval-Augmented Generation (RAG) solutions using enterprise knowledge sources. * Build agentic workflows leveraging modern orchestration frameworks. * Create scalable AI architectures ...

MSRCosmos

AI Solution Architect

Bellevue, WA · On-site

$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 ...

Verathon Inc.

AI Engineer

Bothell, WA · On-site

$115K - $231K/yr

Build Retrieval-Augmented Generation (RAG) pipelines, vector search capabilities, and secure data connectors to enable Verathon-owned data usage. * Collaborate with AI Business Partners and other ...

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Ai Rag information

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$36.4K

$66.3K

$95K

How much do ai rag jobs pay per year?

As of Jul 19, 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.

Which AI is best at RAG?

For an AI Rag role, the best AI systems for Retrieval-Augmented Generation (RAG) tasks typically include models like OpenAI's GPT-4, Google's Bard, and Meta's Llama 2, which are capable of integrating retrieval components with language generation. Success in RAG depends on the model's ability to efficiently access and incorporate external data, as well as the implementation of effective retrieval mechanisms and fine-tuning. Skills in natural language processing, knowledge of retrieval systems, and experience with relevant tools are essential for this role.

What engineer makes 500,000 a year?

Senior software engineers, especially those working in high-demand fields like artificial intelligence or machine learning at large tech companies, can earn $500,000 or more annually. Compensation often includes base salary, bonuses, and stock options, and requires advanced skills, extensive experience, and often a master's or Ph.D. in a related field.

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 data science, deep learning, and experience with tools like TensorFlow or PyTorch, along with a strong track record of innovation and leadership in the field.

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.

Which 3 jobs will survive AI?

AI Rag is a role that involves managing and interpreting AI outputs, and jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to survive AI automation. Examples include healthcare professionals, skilled tradespeople, and roles in education. These jobs often require human judgment, interpersonal skills, and adaptability that AI cannot fully replicate.

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 are popular job titles related to Ai Rag jobs in Seattle, WA? For Ai Rag jobs in Seattle, WA, the most frequently searched job titles are:
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 July 2026, with employment types broken down into 73% Full Time, 23% Part Time, 1% Temporary, and 3% Contract. Highlights an 65% Physical, 4% Hybrid, and 31% 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 • On-site

Full-time

Re-posted 19 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

50th of 451 rated business services

Job description

Job Summary:
Accenture Federal Services is dedicated to enhancing the capabilities of the US federal government through technology and innovation. The Generative AI Applications Engineer will be responsible for developing secure and scalable GenAI applications, focusing on agentic workflows and RAG systems for various federal missions.
Responsibilities:
• 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.
Qualifications:
Required:
• 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
Preferred:
• 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.
Company:
Accenture Federal Services is a leading US federal services company and subsidiary of Accenture. Founded in 1989, the company is headquartered in Arlington, USA, with a team of 10001+ employees. The company is currently Late Stage.

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