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Llm Ml Rag Jobs in Portland, OR (NOW HIRING)

Deloitte

AI Engineer Consultant

Portland, OR · Hybrid

... science/ML, security, and platform engineering to deliver reliable, secure, and scalable AI ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

Hitachi

Data Scientist I or II (MAD-BS-OR)

Hillsboro, OR · On-site +1

$121K - $167K/yr

... Agentic AI / LLM systems, including: * Tool-calling architecture * RAG pipelines * Prompt engineering and evaluation frameworks * Familiarity with: * Distributed systems and scalable ML ...

Deloitte

Google AI Architect

Portland, OR

LLM and AI Integration: Integrate and fine-tune Large Language Models (LLMs) and other AI/ML models ... RAG with Vertex AI Search/Vector Search, prompt design, safety policies, observability). * Deep ...

Intel

Senior AI / Data Science Engineer

Hillsboro, OR

$115K - $156K/yr

Develop and deploy Large Language Model (LLM) and Generative AI applications that improve ... RAG) and Agentic AI architectures. * Experience deploying AI/ML solutions into production ...

All JobsLlm Ml Rag JobsLlm Ml Rag Jobs in Portland, OR

Llm Ml Rag information

See Portland, OR salary details

$47.7K

$79.9K

$116.7K

How much do llm ml rag jobs pay per year?

As of Aug 22, 2026, the average yearly pay for llm ml rag in Portland, OR is $79,856.00, according to ZipRecruiter salary data. Most workers in this role earn between $65,800.00 and $92,300.00 per year, depending on experience, location, and employer.

What is an llm ml rag job?

LLM ML RAG jobs involve working with Large Language Models (LLMs), Machine Learning (ML), and Retrieval-Augmented Generation (RAG) systems. Professionals in these roles typically design, develop, and optimize AI systems that combine language models with retrieval techniques to improve accuracy, relevance, and factual grounding in generated outputs. These jobs often require expertise in natural language processing, deep learning, data engineering, and information retrieval. Key responsibilities might include integrating RAG pipelines, fine-tuning LLMs, and ensuring high-quality responses from AI applications.

What are some typical challenges faced when working on retrieval-augmented generation (RAG) systems in large language model (LLM) machine learning roles?

Professionals working on LLM ML RAG systems often encounter challenges such as ensuring the accuracy and relevancy of retrieved documents, managing latency for real-time queries, and seamlessly integrating retrieval mechanisms with generation models. Additionally, keeping up with evolving datasets and maintaining high-quality knowledge bases can be demanding. Collaboration with data engineers and domain experts is common to refine retrieval pipelines and optimize the end-to-end system.

What are the key skills and qualifications needed to thrive as an llm ml rag engineer, and why are they important?

To excel as an LLM ML RAG Engineer, you need a strong background in machine learning, natural language processing, and large language models, typically supported by a degree in computer science or a related field. Proficiency with tools and frameworks like Python, PyTorch/TensorFlow, Hugging Face Transformers, and vector databases (e.g., FAISS, Pinecone) is essential, along with experience in deploying and fine-tuning LLMs and integrating retrieval systems. Strong problem-solving skills, attention to detail, and the ability to collaborate with cross-functional teams distinguish top performers in this role. These skills ensure the effective development and deployment of advanced AI solutions that combine generative and retrieval capabilities for high-impact applications.

What is the difference between Llm Ml Rag vs Data Scientist?

AspectLlm Ml RagData Scientist
Required CredentialsMaster's or PhD in ML, AI, or related fields; certifications in ML frameworksDegree in Computer Science, Statistics, or related; certifications in data analysis or ML
Work EnvironmentResearch labs, AI development teams, tech companiesBusiness analytics, research, product development teams
Employer & Industry UsageTech firms, AI startups, research institutionsFinance, healthcare, tech, consulting firms
Common Search & ComparisonOften compared for ML specialization and research focusCompared for data analysis, modeling, and business insights

While both roles involve working with machine learning, Llm Ml Rag typically focuses on research and development of large language models, requiring advanced ML expertise. Data Scientists often work on analyzing data, building predictive models, and deriving insights for business decisions. The roles overlap in skills but differ in focus and application areas.

What are popular job titles related to Llm Ml Rag jobs in Portland, OR?

For Llm Ml Rag jobs in Portland, OR, the most frequently searched job titles are:

What job categories do people searching Llm Ml Rag jobs in Portland, OR look for?

The top searched job categories for Llm Ml Rag jobs in Portland, OR are:

What cities near Portland, OR are hiring for Llm Ml Rag jobs?

Cities near Portland, OR with the most Llm Ml Rag job openings:

Llm Ml Rag jobs near you
Infographic showing various Llm Ml Rag job openings in Portland, OR as of August 2026, with employment types broken down into 67% Full Time, and 33% Contract. Highlights an 83% In-person, and 17% Remote job distribution, with an average salary of $79,856 per year, or $38.4 per hour.

Technical Lead - NLP

Noblesoft Technologies

Portland, OR • Remote

Contractor

Re-posted 6 days ago

Job description

Job Role: Senior Technical Lead - NLP
Location: Remote (Support in PST hours)
 
Experience level: 10 + years
Must have skills: SDET skills, Azure devops, python, pytest test automation and Framework, AI ML practices, RAGAS, LLMs
Brief JD:
  • Experience in designing LLM/RAG test automation solutions
  • Experience in testing for bias, drift, and fairness.
  • Familiarity with performance metrics (precision, recall, F1, ROC-AUC).
  • Knowledge of MLflow MLOps framework 
  • Knowledge of tools for synthetic data generation and boundary testing
  • Knowledge of Azure DevOps for CI/CD pipeline development
  • Experience in RAG/pipeline evaluation frameworks - 
    • pytest: Test automation framework
    • DeepEval or TruLens: LLM test assertions
    • RAGAS: RAG-specific metrics
    • Eleuther: LLM evaluation harness
    • Garak or Promptfoo: LLM red-teaming
    • Evidently: Drift/performance monitoring
  • Exposure to explainability frameworks (SHAP, LIME, Captum)
• Handson experience in API and Database testing.
• Practical knowledge of Databricks, Azure Cloud services land distributed data validation.
• Proficiency with Azure DevOps pipelines YAML templates, agent pools, CICD workflows.
• Experience in implementing AIML practices in testing e.g., test generation, anomaly detection, log analysis to improve test efficiency and coverage.
• Familiarity with Cucumber (BDD) and test reporting frameworks (e.g., Allure).
• Strong understanding of integration testing across Databricks streaming jobs, applications and microservices.
• Experience with performance testing tools (JMeter, LoadRunner, or equivalent).
• Experience with Pester framework for validating PowerShell scripts and infrastructure automation.
• Solid understanding of Agile/Scrum methodology and handson usage of JIRA.
• Excellent problem-solving, debugging, and communication skills, with the ability to advise development teams on testing best practices