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Ai Rag Jobs in Eden Prairie, MN (NOW HIRING)

Noblesoft Technologies

AI/ML Engineer

Minneapolis, MN · Remote

$106K - $131K/yr

Familiarity with transformers, LLMs, and Retrieval-Augmented Generation (RAG) pipelines using vector databases. 6. Automation Development: Creating AI-powered automation solutions, including Einstein ...

US Bank

Lead AI Platform Engineer

Minneapolis, MN · On-site

$107K - $140K/yr

Retrieval-Augmented Generation (RAG) architectures * Prompt engineering techniques * Agentic AI workflows and orchestration * Build intelligent systems using frameworks such as LangChain, LangGraph ...

US Bank

Lead AI Platform Engineer

Hopkins, MN · On-site

$104K - $137K/yr

Retrieval-Augmented Generation (RAG) architectures * Prompt engineering techniques * Agentic AI workflows and orchestration * Build intelligent systems using frameworks such as LangChain, LangGraph ...

U.S. Bank

AI Research Scientist

Minneapolis, MN · On-site

Develop solutions leveraging foundation models, generative AI, retrieval-augmented generation (RAG), fine-tuning techniques, and agentic workflows. * Design and evaluate AI agents, multi-agent ...

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Showing results 21-40

All JobsAi Rag JobsAi Rag Jobs in Eden Prairie, MN

Ai Rag information

See Eden Prairie, MN salary details

$32.6K

$59.4K

$85.1K

How much do ai rag jobs pay per year?

As of Aug 8, 2026, the average yearly pay for ai rag in Eden Prairie, MN is $59,384.00, according to ZipRecruiter salary data. Most workers in this role earn between $50,000.00 and $66,300.00 per year, depending on experience, location, and employer.

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 job categories do people searching Ai Rag jobs in Eden Prairie, MN look for? The top searched job categories for Ai Rag jobs in Eden Prairie, MN are:
What cities near Eden Prairie, MN are hiring for Ai Rag jobs? Cities near Eden Prairie, MN with the most Ai Rag job openings:
Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Eden Prairie, MN as of August 2026, with employment types broken down into 77% Full Time, 20% Part Time, and 3% Contract. Highlights an 67% Physical, 3% Hybrid, and 30% Remote job distribution, with an average salary of $59,384 per year, or $28.6 per hour.

Senior AI Engineer - Agentic Systems & Data Pipelines

Collaboration.Ai

Minneapolis, MN • Remote

$108K - $147K/yr

Full-time

Posted 9 days ago

Job description

Who We Are

Collaboration.Ai is a mission-focused, AI-powered software and services company based in Minnesota, with employees, partners, and customers around the world. We unite people, technology, and purpose to accelerate breakthroughs that transform industries, empower communities, and create a more sustainable future. We collaborate with organizations across the defense ecosystem, helping them navigate complex challenges and drive transformative change.

Our Products

NetworkOS — NetworkOS is an AI-powered platform that aligns people, purpose, ideas, and expertise in real-time, generating actionable insights to propel movements forward.

CrowdVector — CrowdVector is an integrated solution marketplace and innovation management platform that rapidly uncovers new ideas and advances breakthroughs to fuel movements.

To learn more about us, visit collaboration.ai.

About the Role

You'll build the agentic systems and data pipelines behind NetworkOS's AI capabilities: production agent workflows built on industry-leading agent SDKs and harnesses, MCP servers, and Agent Skills standards; the eval and observability layer that keeps LLM quality measurable; and the ingestion pipelines that turn messy, diverse data sources into queryable knowledge.

This is an execution seat, not an ivory tower. You'll commit code every week, ship agents as product capability rather than demos, and help shape a roadmap that's heading deep into graph + agents territory — for customers in defense, healthcare, and regulated enterprise.

Agents in production. Pipelines that hold. Evals that keep everyone honest.
This opportunity is remote with a preference for candidates in the Twin Cities area (Minneapolis, Saint Paul); however all candidates are encouraged to apply!

What You'll Do
  • Ship production agent systems — design, build, and operate agentic workflows (agent SDKs, MCP servers, Agent Skills standards) powering AI-driven matching, analysis, and data intelligence
  • Operationalize LLM quality — build the eval and observability layer with Langfuse, golden datasets, LLM-as-judge patterns, and FinOps-style tracking so every workflow has measurable quality, cost, and latency
  • Engineer data pipelines — robust ingestion of documents, structured data, and external sources into searchable knowledge bases with quality validation, deduplication, and incremental updates
  • Own retrieval quality — hybrid search combining vector, keyword, and metadata retrieval, continuously improved through reranking, query expansion, and contextual compression
  • Accelerate with AI — build custom MCP tools and Agent Skills that make the whole engineering team measurably faster
  • Execute alongside the team — pair with full-stack engineers on AI integration points, contribute to incident response for AI services, and keep your hands in the code
Our Tech Stack
  • Languages: Python (primary); Kotlin (core platform language at CAI); TypeScript/Node.js and other modern languages (secondary)
  • AI/ML: FastAPI, Pydantic; multi-provider LLM SDKs (Anthropic, OpenAI, and others)
  • Agentic Tooling: Claude Code/Codex/etc.; industry-leading agent SDKs and harnesses; MCP servers; Agent Skills standards
  • LLM Operations: Langfuse + evals (golden datasets, LLM-as-judge); in-house FinOps tracking (token usage, latency, cost); multi-provider orchestration including AWS Bedrock
  • Search & Retrieval: Vector databases, OpenSearch, embedding models
  • Data: PostgreSQL, Amazon S3; streaming pipelines (Kafka/Kinesis) where needed
  • Infrastructure: Docker, Kubernetes (AWS EKS); DataDog + OpenTelemetry observability
What We're Looking For

Must Haves

  • 7+ years of professional software engineering experience, with 3+ years focused on AI/ML or data engineering
  • Production agentic/LLM application experience — built and operated systems around LLM APIs (Anthropic, OpenAI) serving real users: agents, tool-use, or orchestrated LLM workflows
  • Data engineering background — robust, scalable pipelines for AI/ML workloads
  • LLM operations experience — evals and observability for production LLM systems (quality, cost, latency)
  • Production retrieval experience — vector databases and/or search engines (OpenSearch, Elasticsearch)
  • Modern Python stack proficiency — FastAPI, Pydantic, async/await, modern dependency management
  • AI-native workflows — demonstrated ability to leverage Claude Code/Codex or similar agentic coding tools to accelerate development
  • Experience with Docker, Kubernetes, and AWS
  • US citizenship required (DoD contracting — IL4/IL5 environments — and FedRAMP compliance)
Nice-to-Haves
  • Deep agentic ecosystem experience — Agent Skills standards, custom MCP servers, agent SDKs across major vendors
  • Advanced RAG expertise — GraphRAG, agentic RAG, contextual retrieval, reranking strategies
  • Graph data experience — knowledge graphs, graph databases, or graph-based retrieval
  • Model selection & rightsizing — matching models to domain-specific use cases across quality, cost, and latency tradeoffs
  • Streaming data experience (Kafka, Kinesis) for real-time knowledge base updates
  • Research background, open-source contributions, or an advanced degree in ML/IR/NLP
Why Join Collaboration AI?

Real AI engineering, not a wrapper shop. Production agents, hybrid retrieval, continuous evals, and a roadmap heading into graph + agents — with the autonomy to shape how it's built.

AI-native by default. We build with AI, not just for AI. Agentic coding tools (Claude Code/Codex/etc.), agent SDKs and harnesses, MCP servers, and Agent Skills standards are how we work daily — you'll both use and build them.

Work that matters. Defense, healthcare, and regulated industries — SOC 2 and NIST compliance, FedRAMP readiness, and customers whose missions demand AI they can trust.

Small, senior team. Early-stage impact with your work visible from week one. You'll help set the bar for how AI engineering is done here.

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