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Ai Rag Jobs in Oregon (NOW HIRING)

Defense Unicorns

OR

Defense Unicorns is seeking a Senior Forward Deployed AI Engineer to embed with defense and ... APIs, data processing pipelines, integration layers, Retrieval Augmented Generation (RAG), and ...

Grafana Labs

Staff AI Engineer | US | Remote

OR · Remote

Architect data flows for retrieval-augmented generation (RAG), connecting LLMs to internal ... You'll have access to AI coding assistants (Claude Code, Gemini CLI, OpenAI Codex, and others of ...

Nvidia

OR · On-site

Architect and build enterprise-grade agentic AI systems, RAG pipelines, and multi-modal workflows, ensuring high-performance, GPU-accelerated inference at scale. * Diagnose and resolve intricate ...

phData

OR · On-site

RAG, LLM orchestration, agentic workflows, and modern AI evaluation and guardrails. You see AI tools as essential infrastructure for modern engineering. * You can rapidly prototype with modern tools ...

WEKA

OR

Build integrations for semantic search and Retrieval-Augmented Generation (RAG) workflows ... AI Lifecycle Expertise: Experience across the software stack, including fine-tuning, inference ...

Valtech

Forward Deployed Engineer, Google Cloud, AI Expert

OR · Remote

$55.75 - $74.50/hr

The ideal candidate has shipped agentic AI solutions on Google Cloud, is fluent in Vertex AI and ... Design and implement Retrieval-Augmented Generation (RAG) pipelines and grounding architectures ...

(ISC)2

Deep knowledge of AI/ML concepts and patterns, including machine learning, generative AI, large language models (LLMs), prompt design, retrieval-augmented generation (RAG), model evaluation, and ...

Career Opportunities | (ISC)²

Deep knowledge of AI/ML concepts and patterns, including machine learning, generative AI, large language models (LLMs), prompt design, retrieval-augmented generation (RAG), model evaluation, and ...

Tebra

AI Automation Engineer

OR · Remote

Data/RAG Pipeline Design & Management * Develop and automate ELT/ETL processes to support both BI Analytics and AI retrieval across similar datasets with different access patterns. * Build evaluation ...

Robots and Pencils

OR

$177K - $209K/yr

We're looking for a Staff AI Engineer to lead the design and delivery of AI/ML systems. This role ... Extensive experience building RAG pipelines: chunking strategies, embedding models, vector ...

Cotiviti

$32 - $40/hr

The intern will explore applications of Agentic and Generative AI in healthcare, supporting ... Experience with LLM/RAG models and LLM fine-tuning is a plus. * Hands-on experience working with ...

D.A. Davidson

AI & Automation Engineer

Portland, OR

... RAG) pipelines with vector databases for domain-specific Q&A. Experience with Azure AI Foundry and ... Azure AI capabilities like document intelligence, computer vision, speech, and more. Financial ...

PointClickCare

OR

Prompt Engineering & AI Solution Optimization: -Develop, test, and iterate on prompt engineering strategies to maximize model accuracy, consistency, and performance. -Apply RAG (Retrieval-Augmented ...

NewRocket

AI Data Intelligence Lead

OR · Remote

Experience with semantic retrieval or RAG architecture * Familiarity with ServiceNow data models (CMDB, workflow data, knowledge systems) * Experience in integrating AI solutions with ServiceNow or ...

D.A. Davidson

AI & Automation Engineer

Portland, OR · On-site

$90K - $120K/yr

... RAG) pipelines with vector databases for domain-specific Q&A. Experience with Azure AI Foundry and ... Azure AI capabilities like document intelligence, computer vision, speech, and more. • Financial ...

Chainguard

AI Solutions Engineer

OR · On-site +1

Hands-on experience using AI tools (e.g., Claude Code, Copilot, Cursor) in real work, and building on top of LLM APIs - tool/function calling, agents, prompt and context engineering, RAG, and ...

Instacart

Ads AI Analytics Lead II

OR · Remote

As an Ads AI Analytics Lead II, you will own the intelligence behind our Ads agents. You will ... Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and ...

Vista Equity Partners Management

$200K - $315K/yr

Generative AI including LLMs, diffusion models, and RAG architectures * Agentic architectures, autonomous agents, and multi-agent systems * Cloud AI platforms across AWS, Azure, and GCP * AI model ...

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Ai Rag information

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 Oregon? For Ai Rag jobs in Oregon, the most frequently searched job titles are:
What job categories do people searching Ai Rag jobs in Oregon look for? The top searched job categories for Ai Rag jobs in Oregon are:
What cities in Oregon are hiring for Ai Rag jobs? Cities in Oregon with the most Ai Rag job openings:
Ai Rag jobs near you
Forward Deployed AI Engineer

Forward Deployed AI Engineer

Defense Unicorns

OR

Other

Posted 11 days ago

Job description

EMPLOYER IS A CONTRACTOR FOR THE U.S. GOVERNMENT. THIS POSITION REQUIRES U.S. CITIZENSHIP.

Role Description:

Defense Unicorns is seeking a Senior Forward Deployed AI Engineer to embed with defense and national security customers as a technical partner, owning solutions from problem discovery through production deployment. The work centers on backend engineering, data pipelines, and retrieval systems that power agentic and generative AI capabilities on top of Defense Unicorns' core platform (Unified Defense Stack). You'll deploy and operate these systems across cloud, on-prem, and air-gapped environments, then carry field insights back to product and engineering to improve the platform. This is a customer-facing, delivery-focused role, not an ML research position.

Responsibilities:

  • Embed with strategic customers as a technical partner, owning the arc from problem discovery through solution delivery and customer adoption.
  • Architect, build, and deploy backend systems that power generative AI capabilities: APIs, data processing pipelines, integration layers, Retrieval Augmented Generation (RAG), and context engineering patterns.
  • Deploy and operate systems in cloud, on-prem, and air-gapped environments using Defense Unicorns' core platform and delivery patterns.
  • Scope and sequence delivery: define roadmaps, set priorities, make speed/quality/scope tradeoffs, and remove blockers across internal teams and customer stakeholders.
  • Codify reusable patterns for mission environments: tooling, internal frameworks, and playbooks that make future deployments faster and more repeatable.
  • Serve as the technical face to customer leadership: translate complex technical work for non-technical stakeholders, build trust, and drive adoption.
  • Carry field insights back to product and engineering teams to improve the platform based on what's actually breaking or missing in production.
  • Prototype and validate new capabilities using core products and design patterns, feeding lessons learned back into the product.

Travel Expectations/Requirements: Up to 25%, flexible based on engagement needs

The listed responsibilities are not exhaustive and additional responsibilities may be assigned based on the evolving needs of the organization. We are seeking a dynamic individual who is able to adapt and take on new responsibilities as they arise.

Required Experience and Qualifications:

  • 4+ years of backend or full-stack engineering experience, with production-grade work in Python.
  • Experience designing and operating data pipelines, APIs, and integration layers in production.
  • Hands-on experience with information retrieval systems: keyword search, vector search, document parsing and chunking, reranking, or search engine internals (Elasticsearch, OpenSearch, or similar).
  • Working knowledge of RAG patterns or applied generative AI: connecting LLMs to real data sources in a production context.
  • Experience deploying or operating software in government, defense, or similarly regulated environments (or equivalent high-assurance contexts).
  • Strong communication skills: able to translate technical work into clear language for non-technical stakeholders and build trust with customers.
  • Comfort with ambiguity and shifting priorities. You take initiative, define your own work when direction is unclear, and deliver under pressure.
  • Eligibility to obtain a U.S. security clearance.

Preferred Experience and Qualifications:

  • Experience deploying applications on Kubernetes in air-gapped or classified environments.
  • Familiarity with open-source LLMs/SLMs (Llama, Mixtral, Gemma, Phi), including inference frameworks, structured output, or fine-tuning.
  • Experience developing and maintaining LLM evaluation frameworks.
  • Background in document processing pipelines: parsing, chunking, embedding, and indexing at scale.
  • Active security clearance (Secret or above; TS/SCI preferred).
  • Prior customer-facing or forward-deployed engineering experience.

Apply