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Ai Rag Jobs in Portland, OR (NOW HIRING)

Role Summary Build intelligent capabilities using LLM-based inferencing, agentic AI workflows, and RAG-based solutions leveraging AWS-native AI/ML services. Focus on inference orchestration, vector ...

Collaborate with the AI Infrastructure team to architect robust LLM pipelines, including training workflows and retrieval-augmented generation (RAG) systems * Integrate AI solutions into enterprise ...

Python expertise, with practical experience in LLMs, embeddings, and RAG architecture * 3 years of hands-on AI development experience * Demonstrated experience with generative AI models, including ...

Implement RAG and document intelligence patterns (ingestion, chunking, embeddings, vector/hybrid ... AI Engineer Consultant Our Deloitte Human Capital team transforms technology platforms, drives ...

AI Engineer Senior Consultant

Portland, OR · On-site

$58.50 - $75.50/hr

... RAG, document ingestion/chunking, embeddings, vector/hybrid search, and retrieval/evaluation ... AI engineering, including data modeling, batch/streaming pipelines, structured/unstructured ...

AI Engineer Senior Consultant

Portland, OR · Hybrid

$110.80K - $152.20K/yr

Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ... AI Engineer Senior Consultant Our Deloitte Human Capital team transforms technology platforms ...

Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ... AI Engineer III Position Summary Our Deloitte Human Capital team transforms technology platforms ...

... 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 ...

... 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 ...

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Ai Rag information

See Portland, OR salary details

$33.9K

$61.8K

$88.6K

How much do ai rag jobs pay per year?

As of May 30, 2026, the average yearly pay for ai rag in Portland, OR is $61,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $52,000.00 and $68,900.00 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 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 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.

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.

What are popular job titles related to Ai Rag jobs in Portland, OR? For Ai Rag jobs in Portland, OR, the most frequently searched job titles are:
What cities near Portland, OR are hiring for Ai Rag jobs? Cities near Portland, OR with the most Ai Rag job openings:
AI Engineer

Other

Posted 15 days ago


Job description

Role Summary
Build intelligent capabilities using LLM-based inferencing, agentic AI workflows, and RAG-based solutions leveraging AWS-native AI/ML services. Focus on inference orchestration, vector search pipelines, and lightweight model training for predictive maintenance use cases.
Key Responsibilities
LLM & Inference Engineering:
Develop AI-driven features using LLMs, agentic patterns, RAG, and vector embeddings.
Orchestrate inference pipelines with Python and AWS AI services.
Build reusable components and prompt orchestration flows.
Predictive Analytics & Light Model Training:
Support predictive maintenance using classical ML techniques.
Perform lightweight training with AWS SageMaker, AutoML, and deploy inference endpoints.
AWS Engineering:
Utilize AWS services (Lambda, API Gateway, S3, DynamoDB, SageMaker, Bedrock) for scalable AI workflows.
Python Development:
Write modular, testable Python code for inference orchestration and backend integrations.
Collaboration & Delivery:
Work with product and engineering teams; document AI workflows; participate in design reviews.
Must-Have Skills
Strong proficiency in Python.
Hands-on experience with LLM inferencing, RAG architectures, and vector embeddings.
Working knowledge of AWS AI/ML services (SageMaker, Bedrock, Lambda, etc.).
Familiarity with classical ML concepts (regression, classification, anomaly detection).
Experience integrating models into production pipelines.
Understanding of prompt engineering and evaluation.