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Llm Ml Rag Jobs in Minnesota (NOW HIRING)

Deloitte

AI Engineer Consultant

Minneapolis, MN · 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 ...

THOMSON REUTERS

Lead AI Forward Engineer

Eagan, MN · Remote

$104K - $137K/yr

Evaluate and recommend AI/ML technologies and platforms (LLM orchestration, agentic frameworks ... Familiarity with LLM frameworks and patterns (e.g., LangChain, LlamaIndex), RAG/vector search ...

Thomson Reuters

Lead AI Forward Engineer

Eagan, MN · On-site

$104K - $137K/yr

Evaluate and recommend AI/ML technologies and platforms (LLM orchestration, agentic frameworks ... Familiarity with LLM frameworks and patterns (e.g., LangChain, LlamaIndex), RAG/vector search ...

Vizient

Senior Software Engineer

Minneapolis, MN

$102K - $179K/yr

Collaborate with AI/ML engineers to deploy and support machine learning models, LLM applications, RAG pipelines, and agentic AI solutions in production environments. * Leverage AI-assisted ...

Vizient

Senior Software Engineer

Minneapolis, MN

$102K - $179K/yr

Collaborate with AI/ML engineers to deploy and support machine learning models, LLM applications, RAG pipelines, and agentic AI solutions in production environments. * Leverage AI-assisted ...

Vizient, Inc.

Senior Software Engineer

Edina, MN · On-site

$102K - $179K/yr

Collaborate with AI/ML engineers to deploy and support machine learning models, LLM applications, RAG pipelines, and agentic AI solutions in production environments. * Leverage AI-assisted ...

Vizient, Inc.

Senior Software Engineer

Edina, MN · On-site

$102K - $179K/yr

Collaborate with AI/ML engineers to deploy and support machine learning models, LLM applications, RAG pipelines, and agentic AI solutions in production environments. * Leverage AI-assisted ...

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All JobsLlm Ml Rag JobsLlm Ml Rag Jobs in Minnesota

Llm Ml Rag information

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 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 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 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 popular job titles related to Llm Ml Rag jobs in Minnesota?

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Llm Ml Rag jobs near you

Senior AI Engineer - Agentic Systems & Data Pipelines

Collaboration.Ai

Minneapolis, MN • On-site

$150 - $210/hr

Other

Posted 16 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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