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Ai Rag Jobs in Orem, UT (NOW HIRING)

LVT

Data Engineer, AI

American Fork, UT · On-site +1

$102.30K - $122.90K/yr

Architect and build the data infrastructure required for RAG applications, including vector storage, chunking strategies, and retrieval pipelines. * Collaborate with AI/ML engineers to support ...

LVT

Operational AI Strategy Manager

American Fork, UT · On-site

Build and deploy RAG pipelines and Cortex-based models in partnership with Data Engineering, translating business needs into production-ready AI solutions. * Architect the AI strategy at the ...

LVT

Operational AI Strategy Manager

American Fork, UT · On-site

Build and deploy RAG pipelines and Cortex-based models in partnership with Data Engineering, translating business needs into production-ready AI solutions. * Architect the AI strategy at the ...

LVT

Operational AI Strategy Manager

American Fork, UT · On-site

Build and deploy RAG pipelines and Cortex-based models in partnership with Data Engineering, translating business needs into production-ready AI solutions. * Architect the AI strategy at the ...

LVT

Data Engineer, AI

American Fork, UT · On-site

$102.30K - $122.90K/yr

Architect and build the data infrastructure required for RAG applications, including vector storage, chunking strategies, and retrieval pipelines. * Collaborate with AI/ML engineers to support ...

LVT

Data Engineer, AI

American Fork, UT · On-site +1

$102.30K - $122.90K/yr

Architect and build the data infrastructure required for RAG applications, including vector storage, chunking strategies, and retrieval pipelines. * Collaborate with AI/ML engineers to support ...

LVT Corp

Data Engineer, AI

American Fork, UT · On-site +1

$102.30K - $122.90K/yr

Architect and build the data infrastructure required for RAG applications, including vector storage, chunking strategies, and retrieval pipelines. * Collaborate with AI/ML engineers to support ...

Zions Bank

AI Engineer - Innovation Lab

Midvale, UT · On-site

Develop innovative AI/ML software solutions, specifically focusing on Generative AI, LLMs, and RAG (Retrieval-Augmented Generation) architectures, while adhering to enterprise software standards.

GoodLeap

Principal AI Engineer, Payments

Lehi, UT · On-site

$202K - $237K/yr

Principal Ai Agent Engineer GoodLeap is a technology company delivering best-in-class financing and ... Familiarity with Retrieval-Augmented Generation (RAG) patterns in production systems * Knowledge of ...

GoodLeap

Principal AI Engineer, Payments

Lehi, UT · On-site

Familiarity with Retrieval-Augmented Generation (RAG) patterns in production systems * Knowledge of ... We may use artificial intelligence (AI) tools to support parts of the hiring process, such as ...

GoodLeap

Staff AI Engineer, Payments

Lehi, UT

$173K - $200K/yr

Familiarity with Retrieval-Augmented Generation (RAG) patterns in production systems * Knowledge of ... We may use artificial intelligence (AI) tools to support parts of the hiring process, such as ...

GoodLeap

Principal AI Engineer, Payments

Lehi, UT · On-site

$202K - $237K/yr

Familiarity with Retrieval-Augmented Generation (RAG) patterns in production systems * Knowledge of ... We may use artificial intelligence (AI) tools to support parts of the hiring process, such as ...

GoodLeap

Staff AI Engineer, Payments

Lehi, UT · On-site

Familiarity with Retrieval-Augmented Generation (RAG) patterns in production systems * Knowledge of ... We may use artificial intelligence (AI) tools to support parts of the hiring process, such as ...

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Ai Rag information

See Orem, UT salary details

$27.8K

$50.6K

$72.6K

How much do ai rag jobs pay per year?

As of May 28, 2026, the average yearly pay for ai rag in Orem, UT is $50,637.00, according to ZipRecruiter salary data. Most workers in this role earn between $42,600.00 and $56,500.00 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 Orem, UT? For Ai Rag jobs in Orem, UT, the most frequently searched job titles are:
What job categories do people searching Ai Rag jobs in Orem, UT look for? The top searched job categories for Ai Rag jobs in Orem, UT are:
What cities near Orem, UT are hiring for Ai Rag jobs? Cities near Orem, UT with the most Ai Rag job openings:
Ai Rag jobs near you

Data Engineer, AI

LVT

American Fork, UT • On-site, Remote

$102.30K - $122.90K/yr

Other

Posted 14 hours ago

Job description

ABOUT THIS ROLE

As Data Engineer, AI, you will play a key role in building and advancing LVT's data platform - contributing across the full data engineering lifecycle, from ingestion and transformation to semantic modeling and delivery - while also helping build the data infrastructure that powers LVT's AI initiatives, including RAG pipelines and Snowflake Cortex models. This is a core engineering role with an AI edge: you'll keep the data platform running with precision and reliability, and you'll be the person who makes sure our AI systems have the clean, well-structured data they need to perform.

LVT is a flexible-first company. This role can be performed remotely, with a preference for candidates who can work from our American Fork, UT office.

ROLE RESPONSIBILITIES
  • Design, build, and maintain scalable ELT pipelines that move data reliably from source systems into a clean, well-governed data platform.
  • Develop and maintain semantic models that expose consistent, trusted business definitions across reporting and analytics surfaces.
  • Own data quality - implement validation, monitoring, and alerting frameworks that catch problems before they reach stakeholders.
  • Optimize Snowflake performance across ingestion, transformation, and storage, including dynamic tables, clustering, and query tuning.
  • Architect and build the data infrastructure required for RAG applications, including vector storage, chunking strategies, and retrieval pipelines.
  • Collaborate with AI/ML engineers to support Snowflake Cortex model development with well-structured, context-rich training and inference data.
  • Partner with analytics engineers, data scientists, and business stakeholders to translate requirements into durable data solutions.
  • Document data architecture, pipeline design, and modeling decisions to support team knowledge and long-term maintainability.
OUR IDEAL CANDIDATE
  • Data Engineering Foundation: 5+ years building and maintaining production-grade pipelines using SQL, Python, and modern ELT tools such as dbt, Fivetran, or Airflow.
  • Snowflake Proficiency: Deep hands-on experience with Snowflake, including performance optimization, dynamic tables, and familiarity with Cortex AI capabilities.
  • Semantic Modeling Experience: Ability to design and maintain semantic or metrics layers that enforce consistent business logic across the organization.
  • AI Infrastructure Awareness: Working knowledge of RAG architectures and the data patterns - chunking, embedding, retrieval - that support LLM-driven applications.
  • Data Quality Ownership: Strong instinct for reliability - you instrument pipelines with observability and validation from the start.
  • End-to-End Mindset: Comfortable owning work from scoping through production - you define the problem, build the solution, and stand behind the outcome.
  • Collaborative Communicator: Able to work fluidly with engineers, analysts, and non-technical stakeholders, translating ambiguity into clear data solutions.

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