1

Rag Engineer Jobs in Oregon (NOW HIRING)

Design advanced RAG, Agentic RAG, and multi-agent orchestration architectures leveraging AWS-native ... Engineering Leadership & Delivery Excellence * Provide architectural oversight to Forward Deployed ...

VP, Solutions Architect - AWS

OR · On-site +1

$64.75 - $85/hr

Design advanced RAG, Agentic RAG, and multi-agent orchestration architectures leveraging AWS-native ... Engineering Leadership & Delivery Excellence * Provide architectural oversight to Forward Deployed ...

Senior Forward Deployed Engineer (AI Agent)

OR · On-site +1

$104K - $143K/yr

Strong understanding of general AI agent frameworks, function calling, and retrieval-augmented generation (RAG). Hands-on experience of building such a system is strongly preferred. * Cloud & DevOps: ...

Sitting at the intersection of Product Management, Engineering, and Sales, you will translate deep ... Generative Search & RAG Savvy: A strategic understanding of how Large Language Models (LLMs ...

The AI Legal Engineer is responsible for partnering with subject matter experts to design, build ... Hands-on experience with workflow builders, prompt libraries, RAG pipelines, and AI evaluation ...

About the role We are looking for a Lead Engineer, AI Agent Voice Experience to help build the next ... Experience with RAG systems, agentic workflows, multi-step reasoning systems, or LLM-as-a-judge ...

Vector Database Senior Sales Engineer

OR · On-site +1

$105K - $143K/yr

We are seeking a Subject Matter Expert (SME) Sales Engineer with deep, hands-on expertise in Vector ... Demonstrated expertise in RAG (Retrieval Augmented Generation) architectures and LangChain ...

Forward Deployed AI Solutions Engineer

OR · On-site +1

$105K - $132K/yr

Working knowledge of LLM and agent behavior - prompting, context, tool use, RAG, MCP, evals ... Background in product management, solutions engineering, consulting, forward-deployed engineering ...

Working knowledge of LLM and agent behavior - prompting, context, tool use, RAG, MCP, evals ... Background in product management, solutions engineering, consulting, forward-deployed engineering ...

Sr. Forward Deployed AI Solutions Engineer

OR · On-site +1

$125K - $156K/yr

Working knowledge of LLM and agent behavior - prompting, context, tool use, RAG, MCP, evals ... Background in product management, solutions engineering, consulting, forward-deployed engineering ...

Stay current on emerging technologies, including LLMs, SLMs, retrieval-augmented generation (RAG ... engineering, or consulting within the enterprise software or contact center industry. * Deep ...

OR · Hybrid

$105K - $143K/yr

We are seeking a Subject Matter Expert (SME) Sales Engineer with deep, hands-on expertise in Vector ... Demonstrated expertise in RAG (Retrieval Augmented Generation) architectures and LangChain ...

Experience * 7+ years in a solutions engineering, pre-sales, or sales architecture role, engaged in ... Hands-on ability to configure and demo AI agent flows including intent design, LLM tuning, RAG ...

Showing results 41-60

Rag Engineer information

See Oregon salary details

$62.9K

$95.7K

$162.3K

How much do rag engineer jobs pay per year?

As of Aug 23, 2026, the average yearly pay for rag engineer in Oregon is $95,696.00, according to ZipRecruiter salary data. Most workers in this role earn between $72,400.00 and $111,000.00 per year, depending on experience, location, and employer.

What is the difference between Rag Engineer vs Textile Technician?

AspectRag EngineerTextile Technician
Required CredentialsEngineering degree, technical certificationsDiploma or degree in textiles or related field
Work EnvironmentFactories, manufacturing plants, R&D labsTextile mills, production facilities, quality control labs
Industry UsageDesigning and improving rag production processesMonitoring textile quality, testing fabrics

While both roles involve working within the textile industry, a Rag Engineer primarily focuses on the engineering aspects of rag production, process optimization, and machinery, whereas a Textile Technician concentrates on fabric testing, quality control, and ensuring textile standards are met. The roles often overlap in industry settings but differ in technical focus and responsibilities.

How to become a rag engineer?

To become a rag engineer, you typically need a bachelor's degree in engineering, materials science, or a related field. Relevant skills include knowledge of manufacturing processes, quality control, and proficiency with industry tools and equipment; certifications in quality management or safety can also be beneficial. Gaining experience through internships or entry-level positions in manufacturing environments is important for career advancement.

What are popular job titles related to Rag Engineer jobs in Oregon?

For Rag Engineer jobs in Oregon, the most frequently searched job titles are:

What job categories do people searching Rag Engineer jobs in Oregon look for?

The top searched job categories for Rag Engineer jobs in Oregon are:

What cities in Oregon are hiring for Rag Engineer jobs?

Cities in Oregon with the most Rag Engineer job openings:

Infographic showing various Rag Engineer job openings in Oregon as of August 2026, with employment types broken down into 89% Full Time, 8% Part Time, and 3% Contract. Highlights an 85% Physical, 6% Hybrid, and 9% Remote job distribution, with an average salary of $95,696 per year, or $46 per hour.

Senior Solutions Architect

Robots and Pencils

OR • On-site, Remote

Full-time

Re-posted 2 hours ago


Job description

Robots & Pencils is seeking a seasoned AWS AI Solutions Architect to lead the design and delivery of complex, enterprise-grade generative and agentic AI systems built on Amazon Web Services. You will architect scalable, secure, and production-ready AI platforms leveraging Amazon Bedrock, Amazon Bedrock AgentCore, AWS Strands Agents, AWS AgentCore Gateway, Nova Forge, Nova 2 Sonic, and related AWS AI/ML services. 
As an AWS AI Solutions Architect, you will serve as a strategic technical advisor-translating ambiguity into structured AWS-native architectures, validating designs through hands-on prototyping, and ensuring every solution aligns with the AWS Well-Architected Framework (including ML Lens) while delivering measurable business value. 

Key Responsibilities 

Client Engagement & AWS Solutions Architecture 

  • Serve as the primary AWS AI architecture partner for strategic clients, driving generative and agentic AI system design from discovery through production. 
  • Lead architecture design using Amazon Bedrock (including foundation models and custom models), Bedrock AgentCore, AWS Strands Agents, and AWS AgentCore Gateway. 
  • Design advanced RAG, Agentic RAG, and multi-agent orchestration architectures leveraging AWS-native services such as Lambda, Step Functions, API Gateway, DynamoDB, Aurora (pgvector), and OpenSearch. 
  • Produce AWS reference architectures, architecture decision records (ADRs), and implementation roadmaps aligned to business objectives. 
  • Validate feasibility through hands-on prototyping in Python using Bedrock SDKs, SageMaker, and serverless services. 
  • Ensure architectures follow AWS security best practices (IAM, KMS, VPC, PrivateLink) and cost optimization principles. 

Outcome Ownership & Business Impact 

  • Own architectural integrity from concept through production deployment on AWS. 
  • Align solutions with AWS Well-Architected Framework pillars: Operational Excellence, Security, Reliability, Performance Efficiency, Cost Optimization, and Sustainability. 
  • Guide clients through tradeoff decisions across model selection (Bedrock FMs vs custom SageMaker models), latency, cost, governance, and compliance. 
    Accelerate time-to-value through reusable AWS accelerators, Infrastructure as Code (CloudFormation/Terraform/CDK), and CI/CD automation. 
  • Continuously evaluate emerging AWS AI capabilities (Nova Forge, Nova 2 Sonic, Bedrock updates, and new AgentCore capabilities). 

Engineering Leadership & Delivery Excellence 

  • Provide architectural oversight to Forward Deployed Engineers and AWS delivery teams. 
  • Establish best practices for MLOps on AWS including model lifecycle management, monitoring, and observability using SageMaker, CloudWatch, CloudTrail, and AWS Config. 
  • Define governance, responsible AI guardrails, Bedrock Guardrails configuration, and security controls for enterprise environments. 
  • Mentor engineers on AWS AI service integration, distributed systems design, and secure multi-account strategies. 
  • Make principled tradeoffs under constraints related to privacy, compliance (SOC2, HIPAA, GDPR), cost, and operational complexity. 

Cross-Functional Collaboration 

  • Partner with internal product, engineering, research, and customer success teams to evolve AWS-based AI offerings. 
  • Contribute AWS reference architectures and reusable infrastructure modules to internal accelerators. 
  • Support pre-sales engagements including architecture workshops, AWS migration strategy, and solution scoping. 
  • Collaborate across distributed teams and client stakeholders across North America. 

Required Skills & Qualifications 

  • Bachelor's degree in Computer Science, Engineering, or equivalent experience.
  • 7-10+ years of experience in software engineering or cloud architecture with deep AWS ownership. 
  • Deep expertise in Amazon Bedrock, Bedrock AgentCore, AWS Strands Agents, AgentCore Gateway, and related AWS AI services. 
  • Strong familiarity with SageMaker (training, deployment, pipelines), deep learning fundamentals, and model fine-tuning strategies. 
  • Experience architecting RAG, multi-agent, and orchestration systems using AWS-native services. 
  • Strong knowledge of distributed systems, event-driven architectures, and serverless patterns. 
  • Proficiency with Infrastructure as Code (AWS CDK, CloudFormation, Terraform). 
  • Hands-on development capability in Python and AWS SDKs. 
  • Experience implementing observability and monitoring strategies in AWS environments. 
  • Proven success leading enterprise-scale AWS transformations.
  • Exceptional communication skills for both technical and executive audiences. 
  • AWS Professional Certifications highly preferred (AWS Solutions Architect - Professional, AWS DevOps Engineer - Professional). 

Nice to Have 

  • AWS Specialty certifications (Machine Learning - Specialty, Security - Specialty).
  • Experience with advanced agentic reasoning patterns (ReAct,CoT, Tree-of-Thoughts) implemented on Bedrock. 
  • Experience building secure multi-account AWS organizations using Control Tower. 
  • Exposure to data engineering services such as Glue, Redshift, Lake Formation. 
  • Consulting or professional services background. 

Personal Competencies 

  • Accountability - Owns AWS architectural direction and client outcomes with rigor.
  • Adaptability - Rapidly adopts new AWS AI releases and evolving generative AI capabilities.
  • Collaboration - Builds trust across engineering and executive stakeholders. 
  • Execution-Focused - Balances innovation with production-ready AWS delivery. 
  • Innovation-Minded - Experiments responsibly with emerging AWS AI services. 
  • Craftsmanship - Designs secure, scalable, and well-documented AWS systems. 
  • Leadership with Courage - Drives architectural alignment in complex environments. 
  • Comfort in Ambiguity - Translates unclear AI requirements into AWS-native solution architectures. 

Why Join Robots & Pencils? 

We build smart systems for a human world - blending creativity, engineering, and AWS-powered AI to help organizations reimagine how they work. As an AWS AI Solutions Architect, you will shape enterprise-scale generative and agentic AI platforms using the most advanced AWS services available. You will define architectures that deliver measurable business value, mentor teams, and directly influence the evolution of our AWS AI practice.