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Ai Rag Jobs in Minnesota (NOW HIRING)

Does not act as the primary technical owner for RAG frameworks, vector databases, or embedding ... AI solutions benefit from more relevant, uptodate, and understandable data . * Clear ownership and ...

AI/ML Engineer

Minneapolis, MN · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Design and develop GenAI solutions using prompt engineering, Context Engineering, Retrieval-Augmented Generation (RAG), and custom pipelines * Design and develop interoperable AI agents using Model ...

New

AI Engineer (Hybrid)

Saint Paul, MN · On-site

$105K - $145K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

... RAG frameworks, and model hosting options. * Rapidly experiment with new AI services, foundation models, and developer tooling to assess maturity, extensibility, and alignment with long-term ...

Design robust AI solution architectures including expertise in designing robust Agentic GenAI systems, robust ML & RAG pipelines, Context Engineering pipelines, agentic orchestration, model context ...

Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability. * Define end-to-end architectures across data ...

We are hiring an AI Engineer to build and operate the data, features, and GenAI foundations that ... Implement LLM application patterns including RAG, document ingestion/chunking, embeddings, vector ...

  • Medical

  • Dental

  • Vision

  • Retirement

Experience building RAG-based systems, vector databases, and semantic search architectures. * Demonstrated ability to lead large-scale AI initiatives and influence technical strategy. * Deep ...

Architect - Agentic AI

Maplewood, MN · On-site

$188K - $230K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Experience with RAG/memory architecture (chunking, embeddings, vector DBs, enterprise search ... Service/AI foundry) and routing across multiple model providers. * Familiarity with agent ...

Architect - Agentic AI

Maplewood, MN · On-site

$188K - $230K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Experience with RAG/memory architecture (chunking, embeddings, vector DBs, enterprise search ... Service/AI foundry) and routing across multiple model providers. * Familiarity with agent ...

Architect - Agentic AI

Maplewood, MN · On-site

$188K - $230K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Experience with RAG/memory architecture (chunking, embeddings, vector DBs, enterprise search ... Service/AI foundry) and routing across multiple model providers. * Familiarity with agent ...

Architect - Agentic AI

Maplewood, MN

$188K - $230K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Experience with RAG/memory architecture (chunking, embeddings, vector DBs, enterprise search ... Service/AI foundry) and routing across multiple model providers. * Familiarity with agent ...

Senior AI/ML Engineer

Eden Prairie, MN · On-site

$106K - $146K/yr

  • Retirement

Design and implement retrieval-augmented generation (RAG) pipelines including document ingestion, chunking strategies, embedding generation, and vector database integration * Build agentic AI systems ...

Senior AI/ML Engineer

Eden Prairie, MN · On-site +1

$106K - $146K/yr

  • Retirement

Design and implement retrievalaugmented generation (RAG) pipelines including document ingestion, chunking strategies, embedding generation, and vector database integration * Build agentic AI systems ...

Staff Software Engineer - AI II

Eagan, MN · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Build enterprise-grade AI systems - including domain-specific agent frameworks and advanced RAG architectures. * Drive AI innovation by leading proofs of concept, evaluating emerging techniques, and ...

AI Engineer

Minnetonka, MN · On-site

$100K - $110K/yr

  • Medical

  • Dental

  • Vision

  • Retirement

  • PTO

Solid grasp of machine learning and AI concepts, model behavior, and experience with NLP or ... RAG and Data Handling: Familiarity with embedding models, vector databases, and unstructured data ...

Showing results 41-60

Ai Rag information

What are the key skills and qualifications needed to thrive as an AI researcher?

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.

What is an AI RAG?

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 are common challenges faced by AI RAG 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 Minnesota? For Ai Rag jobs in Minnesota, the most frequently searched job titles are:
What job categories do people searching Ai Rag jobs in Minnesota look for? The top searched job categories for Ai Rag jobs in Minnesota are:
What cities in Minnesota are hiring for Ai Rag jobs? Cities in Minnesota with the most Ai Rag job openings:

Full-time

Medical, Life

Re-posted 20 days ago


Job description

Are you passionate about improving data quality and readiness to unlock the full potential of AI solutions?

Do you enjoy collaborating across teams to ensure data is structured, governed, and usable for intelligent systems?

About the Business:

LexisNexis Risk Solutions is the essential partner in the assessment of risk. Within our Business Services vertical, we offer a multitude of solutions focused on helping businesses of all sizes drive higher revenue growth, maximize operational efficiencies, and improve customer experience. Our solutions help our customers solve difficult problems in the areas of Anti-Money Laundering/Counter Terrorist Financing, Identity Authentication & Verification, Fraud and Credit Risk mitigation and Customer Data Management. You can learn more about LexisNexis Risk at the link below,

https://risk.lexisnexis.com

About the Team:

We are a newly formed Enterprise AI team focused on enabling agent-based solutions across the organization. We build and manage the environments, platforms, and guardrails that allow teams to create, test, and scale AI agents safely and efficiently turning experimentation into real business impact.

We're a team of curious builders and operators who are constantly exploring, learning, and applying new AI tools and approaches to solve real-world problems and improve how work gets done.

About the Role:

We are seeking an AI Data Analyst to support teams in preparing and maintaining AIready data for use in AI tools, copilots, and intelligent agents. This role focuses on data readiness, quality, metadata, and governance, helping teams understand how to structure, document, and manage their data so it can be safely and effectively used by AI systems.

The AI Data Analyst partners with data engineering, AI, and governance teams to assess data readiness, identify gaps and recommend improvements. This role does not own endtoend data pipelines and is not expected to be a deep technical expert in RAG or embeddings, but should have a solid working understanding of AIdriven data needs.

Responsibilities:

AI Data Readiness Support

  • Work with product and delivery teams to assess whether datasets and content are fit for AI use cases.
  • Help teams understand and apply AI data readiness standards, including quality, freshness, metadata, and access expectations.
  • Identify common data issues that impact AI outcomes (e.g., stale data, unclear ownership, missing metadata) and recommend remediation steps.
  • Contribute to repeatable checklists, guidance, or documentation that help teams prepare data for AI.

Data Quality & Relevance

  • Support data quality checks focused on accuracy, completeness, consistency, and timeliness for AIconsumed data.
  • Assist in monitoring and validating data freshness and relevance, escalating issues to engineering or data owners as needed.
  • Help teams improve data clarity and usability to reduce ambiguity in AI outputs.

Metadata & Semantic Enablement

  • Assist teams in improving metadata, documentation, and business descriptions so AI systems can better interpret content.
  • Support basic semantic labeling or categorization efforts that improve AI retrieval and reasoning (in coordination with engineering teams).
  • Promote good content hygiene practices (clear structure, consistent naming, wellscoped documents).

AI Data Sources & Retrieval (Support Role)

  • Support the upkeep and documentation of approved data sources used by AI solutions.
  • Help ensure data included in AI retrieval scenarios is appropriate, governed, and up to date.
  • Collaborate with AI and platform teams on data inclusion/exclusion decisions without owning technical implementation.

Governance, Lineage & Compliance Awareness

  • Help teams align AIconsumed data with enterprise governance requirements, including classification, access controls, and retention.
  • Support basic data lineage and ownership documentation for AIrelevant datasets.
  • Partner with governance and security teams by surfacing risks or gaps; does not act as final approval authority.

What This Role Does Not Own

  • Does not design or own endtoend production data pipelines.
  • Does not act as the primary technical owner for RAG frameworks, vector databases, or embedding strategies.
  • Does not make final governance or compliance decisions independently.

Requirements:

  • Proven experience in data analysis, analytics engineering, data operations, or data quality roles.
  • Good understanding of data quality principles and how poor data impacts downstream systems.
  • Experience working with structured and unstructured data (tables, files, documents, knowledge assets).
  • Proficiency in SQL and comfort investigating data issues.
  • Familiarity with data governance fundamentals (classification, access controls, ownership, retention).
  • Strong communication skills and ability to explain data concepts to nontechnical stakeholders.

Preferred Qualifications

  • Exposure to AIenabled products, copilots, or searchbased solutions.
  • Basic familiarity with AI data concepts such as semantic search, embeddings, or retrieval patterns.
  • Experience working in enterprise or regulated environments.
  • Experience contributing to standards, playbooks, or shared data practices.

What Success Looks Like

  • Teams can reliably prepare datasets that meet AI readiness expectations with less rework.
  • AI solutions benefit from more relevant, uptodate, and understandable data.
  • Clear ownership and documentation exist for data used by AI systems.
  • Strong collaboration between delivery teams, data engineering, and governance.

Working for You:

We know that your wellbeing and happiness are key to a long and successful career. These are some of the benefits we are delighted to offer:

  • Medical Inpatient and Outpatient Insurance: Coverage for your healthcare needs.
  • Life Assurance Policies: Providing financial security for your loved ones.
  • Modern Family Benefits: Support for maternity, paternity, and adoption needs.
  • Long Service Award: Recognition for your dedication and loyalty.
  • Celebratory Allowance/Gifts: Marking special occasions to celebrate with you.
  • Flexible Benefits Plan : Offering you wider choice of services and products
  • Employee Assistance Program : Access support for personal and work-related challenges.
  • Flexible Working Arrangements: Balance work and personal life effectively.
  • Access to Learning and Development Resources: Empowering your professional growth.

Risk benefit statement
Learn more about the LexisNexis Risk team and how we work: https://relx.wd3.myworkdayjobs.com/RiskSolutions/page/21c296c982531000b79663f3194b0000

U.S. National Base Pay Range: $78,800 - $131,300. Geographic differentials may apply in some locations to better reflect local market rates. If performed in Ohio, the base pay range is $74,900 - $124,700. This job is eligible for an annual incentive bonus.

We know your well-being and happiness are key to a long and successful career. We are delighted to offer country specific benefits. Click here to access benefits specific to your location.

We are committed to providing a fair and accessible hiring process. If you have a disability or other need that requires accommodation or adjustment, please let us know by completing our Applicant Request Support Formor please contact 1-855-833-5120.

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We are an equal opportunity employer: qualified applicants are considered for and treated during employment without regard to race, color, creed, religion, sex, national origin, citizenship status, disability status, protected veteran status, age, marital status, sexual orientation, gender identity, genetic information, or any other characteristic protected by law.

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