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Rag Jobs in Oregon (NOW HIRING)

Tebra

Senior AI Automation Engineer

OR · On-site +1

$103K - $136K/yr

Data/RAG Pipeline Design & Management * * Design and automate data flow processes to support AI retrieval (RAG) and insights generation across core enterprise platforms. * Build evaluation into every ...

LTS

Lead AI Architect/Strategist

OR · On-site +1

Design and evaluate RAG architectures, including data ingestion, embeddings, vector search, retrieval strategies, and model orchestration. * Lead technical evaluations of emerging AI technologies ...

LTS

AI Platform and Harness Engineer

OR · On-site +1

Design, build, and maintain enterprise AI platform capabilities supporting Large Language Models (LLMs), AI agents, RAG, and Generative AI applications. * Develop reusable AI harnesses to automate ...

Robots and Pencils

Senior Solutions Architect

OR · On-site +1

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.

Robots and Pencils

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 services such as Lambda, Step Functions, API Gateway, DynamoDB, Aurora (pgvector), and OpenSearch.

EM Key Solutions Inc

AI Engineer

OR · On-site +1

Build AI Copilots, AI Agents, and Retrieval-Augmented Generation (RAG) solutions to improve software engineering, testing, documentation, and operational workflows. * Develop intelligent automation ...

Yubico Inc.

Sr. Director Product Marketing

OR · On-site +1

Generative Search & RAG Savvy: A strategic understanding of how Large Language Models (LLMs), vector databases, and Retrieval-Augmented Generation (RAG) systems crawl, parse, and recommend technical ...

EM Key Solutions Inc

Temporary AI Engineer

OR · On-site +1

Build AI Copilots, AI Agents, and Retrieval-Augmented Generation (RAG) solutions to improve software engineering, testing, documentation, and operational workflows. * Develop intelligent automation ...

Particle41

AI Engineer

OR · On-site +1

Build generative-AI solutions (RAG, Agentic Workflows, MCP Servers, Conversation AI Agents) aligned with business goals. * Work closely with data engineering teams to build/maintain data pipelines ...

phData

Applied AI Solutions Architect

OR · On-site +1

$63 - $83/hr

Strong understanding of Applied AI and modern machine learning systems, including predictive ML, MLOps, generative AI, LLM applications, RAG, and agentic architectures. * Hands-on experience with ...

ClickHouse

Senior Software Engineer - Integrations - AI/ML

OR · On-site +1

$122K - $161K/yr

The data practitioner's world is shifting rapidly: databases are no longer just query targets, but they're becoming active participants in AI-powered workflows, serving as vector stores for RAG ...

LTS

Senior Applied AI Engineer

OR · On-site +1

$122K - $161K/yr

Continuously refine Retrieval-Augmented Generation (RAG) pipelines, retrieval strategies, embeddings, reranking, and grounding techniques. * Improve AI response quality through experimentation ...

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All JobsRag JobsRag Jobs in Oregon

Rag information

See Oregon salary details

$42.8K

$83.3K

$125.3K

How much do rag jobs pay per year?

As of Aug 22, 2026, the average yearly pay for rag in Oregon is $83,265.00, according to ZipRecruiter salary data. Most workers in this role earn between $64,500.00 and $98,900.00 per year, depending on experience, location, and employer.

What is a RAG?

RAG stands for Retrieval-Augmented Generation, a model architecture that combines information retrieval with generative AI. In this role, a RAG specialist or engineer works on designing, implementing, and optimizing systems that retrieve relevant data from large databases to provide more accurate and informed AI-generated responses. This position typically requires strong knowledge of natural language processing, information retrieval, and deep learning frameworks. RAG models are particularly useful in applications like customer support, search engines, and knowledge management systems.

What skills and qualifications are needed to thrive as a RAG engineer?

To thrive as a Retrieval-Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and software engineering, often with a degree in computer science or a related field. Familiarity with frameworks like PyTorch or TensorFlow, experience with vector databases, and knowledge of APIs for language models are typically required. Problem-solving, effective communication, and adaptability are crucial soft skills for collaborating with teams and navigating evolving technologies. These skills are important to successfully develop, deploy, and maintain RAG systems that enhance the performance and relevance of AI-driven applications.

What are common challenges faced by RAG engineers when integrating retrieval systems with large language models?

RAG engineers often encounter challenges in ensuring the seamless integration of retrieval systems with large language models, such as maintaining low latency while fetching relevant documents and ensuring retrieved data is contextually appropriate for generation tasks. Balancing retrieval accuracy and computational efficiency is key, especially when dealing with large-scale or real-time applications. Effective collaboration with data engineers, NLP researchers, and product teams is essential to continuously refine retrieval pipelines and improve the relevance of generated outputs.

What is the difference between Rag vs Data Analyst?

AspectRagData Analyst
Required CredentialsVaries, often no formal degreeBachelor's degree in data-related field, often certifications
Work EnvironmentFieldwork, on-site, or warehouse settingsOffice-based, computer-focused
Employer & Industry UsageConstruction, manufacturing, logisticsFinance, marketing, healthcare, tech
Common Search & ComparisonRag vs Data AnalystData Analyst roles and responsibilities

While Rags typically work in physical environments handling materials or equipment, Data Analysts focus on interpreting data to inform business decisions. Both roles require analytical skills but differ significantly in credentials, work setting, and industry applications.

What are the most commonly searched types of Rag jobs in Oregon?

The most popular types of Rag jobs in Oregon are:

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

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

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

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

Rag jobs near you
Infographic showing various Rag job openings in Oregon as of August 2026, with employment types broken down into 57% Full Time, and 43% Contract. Highlights an 100% In-person job distribution, with an average salary of $83,265 per year, or $40 per hour.

Senior AI Automation Engineer

Tebra

OR • On-site, Remote

$103K - $136K/yr

Full-time

Re-posted 20 days ago

Job description

About the Role

We are seeking a hands-on AI & Automation Engineer to design, build, and optimize integrations, automations, and advanced AI workflows, including agentic architecture, that power our enterprise business operations. You'll own the Workato automation stack, engineer scalable AI workflows on top of core platforms (Salesforce, NetSuite, Snowflake, Slack), and implement agentic capabilities that allow systems to act as intelligent tools, reducing manual work and accelerating insights.

This role requires deep technical execution skills in integration development, agentic system design, and programming, combined with a practical understanding of AI workflows. You will collaborate directly with stakeholders to translate business needs into scalable, automated solutions and deliver measurable improvements in system efficiency and operational performance.

Your Area of Focus

Integration & Automation Development

    • Design, build, and maintain Workato recipes, connectors, and orchestrations for Salesforce, NetSuite, Slack, and Snowflake.
    • Implement error handling, observability, and reusable design patterns to ensure reliability and scalability.

Agentic System Design

    • Architect multi-agent systems: tool selection, planning loops, state management, human-in-the-loop Checkpoints.
    • Partner with stakeholders to design corporate systems (Salesforce, NetSuite, Snowflake, Slack, etc) as agent-callable tools, not just data sources.
    • Build guardrails, fallbacks and error recovery for non-deterministic workflows.

Data/RAG Pipeline Design & Management

    • Design and automate data flow processes to support AI retrieval (RAG) and insights generation across core enterprise platforms.
    • Build evaluation into every system to ensure high quality results, considering precision, answer faithfulness, latency, and cost.
    • Own the Retrieval-Augmented Generation (RAG) lifecycle end-to-end, including ingestion, chunking strategy, embeddings, vector storage, and reranking.
    • Implement data quality checks and architectural safeguards to ensure trusted, high-fidelity datasets for AI agents.

AI Operations

    • Production observability: prompt/response tracing, cost monitoring, eval dashboards.
    • Document agent behavior, decision logic and failure modes.
    • A/B testing agent versions, model comparisons.

Programming for Automation

    • Write modular, reusable scripts in Python, Ruby, SQL, or JavaScript to support integration, data transformation, and automation tasks.

Governance & Documentation

    • Apply data governance practices for lineage, security, and retention.
    • Maintain technical documentation, diagrams, and version control via GitHub, Confluence, and Jira.
Your Professional Qualifications
  • 5+ years of experience in integration, automation, AI, or software engineering, with 1+ year hands-on in Workato.
  • Proven expertise with Enterprise iPaaS required (Workato, Mulesoft, or similar).
  • Deep proficiency in designing, implementing, and architecting complex AI workflows and integrations across core platforms (Salesforce, NetSuite, Slack, and Snowflake).
  • Hands-on technical background in programming (Python, Ruby, or similar), leveraging APIs to build and maintain scalable, high-fidelity enterprise systems.
  • Experience using vector databases such as Pinecone, Snowflake Cortex Search, and pgvector for Retrieval-Augmented Generation (RAG).
  • Experience with AI/ML concepts and implementing agentic architecture and AI workflows in enterprise environments.
  • Experience with Cloud Platforms such as GCP or AWS preferred.
  • Experience with GitHub for version control, Jira for work tracking, and Confluence/Lucid for documentation.
  • Strong problem-solving, critical thinking, and ability to execute under minimal supervision.
  • Motivated to learn new technologies and develop new skills.
  • Track record of introducing innovation in automation and AI while maintaining governance and system reliability.
  • Workato Automation Pro , Foundations or SnowPro certifications preferred.

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