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Rag Developer 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

Strong programming skills in Python and familiarity with libraries such as NumPy, Pandas, and ... Proficiency in RAG (Retrieval-Augmented Generation) techniques. Strong understanding of natural ...

New

Programmers.io

Azure AI/ML Engineer

Bellevue, WA · On-site

$62 - $77/hr

... DevOps,Docker,Kubernetes Role Descriptions: 8 years of Strong proficiency in Python| C| or Java Hands-on experience in building RAG-based systems and implement RAG pipelines using embeddings| vector ...

Futran Tech Solutions Pvt. Ltd.

.Net Developer

Redmond, WA · On-site

$54.75 - $72.50/hr

Advanced AI Tech: Hands-on with RAG (Retrieval-Augmented Generation) and MCP (Model Context ... DevOps & Operational Excellence * Implement CI/CD pipelines to enable rapid and reliable ...

Merican

Sr Full Stack Java Developer

Bellevue, WA · On-site

$59.25 - $76.50/hr

Familiarity with foundation models, prompt engineering, retrieval-augmented generation (RAG), and AI agent development applied to full-stack application development, including enterprise reusable ...

Qaurs Techno Systems LLC

Senior AI Engineer

Bellevue, WA · On-site

$75K - $85K/yr

The ideal candidate will have strong backend/full-stack engineering expertise, hands-on experience building LLM-powered applications, RAG architectures, agentic workflows, and enterprise-scale data ...

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Rag Developer information

What engineers make $500,000?

Senior engineers in specialized fields such as software engineering, data engineering, or engineering management can earn $500,000 or more annually, especially with extensive experience, advanced skills, and in high-demand industries like technology or finance. Compensation often includes base salary, bonuses, and stock options, particularly at large tech companies or startups with significant growth potential.

What is the difference between Rag Developer vs Textile Technician?

AspectRag DeveloperTextile Technician
CredentialsTypically requires a diploma or degree in textiles or related fieldRequires similar qualifications, often with additional certifications in textile testing
Work EnvironmentFactories, textile mills, production plantsLaboratories, quality control departments, manufacturing facilities
Industry UsageUsed in textile manufacturing to develop and process rags for reuse or recyclingInvolved in testing, quality assurance, and technical support in textile production

Both Rag Developers and Textile Technicians work within the textile industry, often in manufacturing settings. Rag Developers focus on creating and processing recycled rags, while Textile Technicians handle testing and quality control. The roles share similar educational backgrounds and work environments, but their specific responsibilities differ based on their focus within textile production.

What does a RAG engineer do?

A RAG (Red, Amber, Green) engineer develops and maintains systems that use RAG status indicators to monitor project or system health. They often work with data visualization tools, automate status reporting, and analyze performance metrics to support decision-making. Strong skills in data analysis, programming, and understanding of project management are typically required.

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 they may involve leadership responsibilities or specialized expertise in cutting-edge AI technologies.

Which 3 jobs will survive AI?

For a Rag Developer, roles that require complex manual craftsmanship, creative problem-solving, and specialized knowledge are more likely to persist despite AI advancements. Jobs involving intricate textile design, custom tailoring, and quality inspection rely on human skills and judgment that AI cannot fully replicate. Developing expertise in these areas, along with staying updated on industry tools, can help ensure job security.
What are popular job titles related to Rag Developer jobs in Seattle, WA? For Rag Developer jobs in Seattle, WA, the most frequently searched job titles are:
What job categories do people searching Rag Developer jobs in Seattle, WA look for? The top searched job categories for Rag Developer jobs in Seattle, WA are:
What cities near Seattle, WA are hiring for Rag Developer jobs? Cities near Seattle, WA with the most Rag Developer job openings:
Rag Developer jobs near you
Infographic showing various Rag Developer job openings in Seattle, WA as of July 2026, with employment types broken down into 100% Contract. Highlights an 60% In-person, and 40% Remote job distribution.
Generative AI Applications Engineer (Agents & RAG)

Generative AI Applications Engineer (Agents & RAG)

Accenture Federal Services

Seattle, WA • On-site

Full-time

Re-posted 17 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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