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Ai Rag Jobs in Minnesota (NOW HIRING)

Digital Links Inc

AI/ML Engineer

Minneapolis, MN · On-site

This role requires strong expertise in Generative AI, RAG (Retrieval-Augmented Generation), and enterprise integrations. The ideal candidate should be capable of independently delivering scalable AI ...

Magicforce

AI QA Engineer

Hopkins, MN · On-site

Skills - Quality LLM, OpenAI, Azure, Python RAG Pipeline, AgenticAI. Looking for a AI Quality Engineering Lead with expertise in LLMs (OpenAI, Azure OpenAI), Agentic AI, RAG pipelines, Python ...

ITR Group

Junior AI Engineer

Minneapolis, MN · On-site

Opportunity available for Junior AI Engineer to join a growing AI team and help build enterprise Generative AI applications using Azure AI services , Retrieval-Augmented Generation (RAG) , and modern ...

UnitedHealth Group

AI Engineer - AI/ML

Minnetonka, MN · On-site

$116K - $140K/yr

Build Multi Agentic workflows and RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases * Leverage AWS Bedrock and Google Vertex AI for scalable and production-grade GenAI ...

UnitedHealth Group

AI Engineer - AI/ML

Minnetonka, MN · Hybrid

$116K - $140K/yr

Build Multi Agentic workflows and RAG (Retrieval-Augmented Generation) pipelines for enterprise use cases * Leverage AWS Bedrock and Google Vertex AI for scalable and production-grade GenAI ...

THOMSON REUTERS

Lead Software Engineer, AI

Eagan, MN · Hybrid

As a Lead Software Engineer - AI, you will play a lead role in full-stack web development, focusing on AI solutions, agents, and RAG, as well as traditional application layers such as gateways, APIs ...

Thomson Reuters

Lead Software Engineer, AI

Eagan, MN · On-site

As a Lead Software Engineer - AI, you will play a lead role in full-stack web development, focusing on AI solutions, agents, and RAG, as well as traditional application layers such as gateways, APIs ...

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Ai Rag information

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 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.

Which AI is best at RAG?

For an AI Rag role, the best AI systems for Retrieval-Augmented Generation (RAG) tasks typically include models like OpenAI's GPT-4, Google's Bard, and Meta's Llama 2, which are capable of integrating retrieval components with language generation. Success in RAG depends on the model's ability to efficiently access and incorporate external data, as well as the implementation of effective retrieval mechanisms and fine-tuning. Skills in natural language processing, knowledge of retrieval systems, and experience with relevant tools are essential for this role.

What engineer makes 500,000 a year?

Senior software engineers, especially those working in high-demand fields like artificial intelligence or machine learning at large tech companies, can earn $500,000 or more annually. Compensation often includes base salary, bonuses, and stock options, and requires advanced skills, extensive experience, and often a master's or Ph.D. in a related field.

What is a $900000 AI job?

A $900,000 AI job typically refers to a high-paying position in artificial intelligence, such as senior machine learning engineer, AI research director, or executive roles like AI CTO. These roles often require advanced skills in data science, deep learning, and experience with tools like TensorFlow or PyTorch, along with a strong track record of innovation and leadership in the field.

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.

Which 3 jobs will survive AI?

AI Rag is a role that involves managing and interpreting AI outputs, and jobs that require complex problem-solving, creativity, and emotional intelligence are more likely to survive AI automation. Examples include healthcare professionals, skilled tradespeople, and roles in education. These jobs often require human judgment, interpersonal skills, and adaptability that AI cannot fully replicate.

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 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:
Ai Rag jobs near you

AI/ML Engineer

Digital Links Inc

Minneapolis, MN • On-site

Contractor

Re-posted 21 days ago

Job description

Job Description:

 
Role: AI/ML Engineer 
Duration: 3-month contract (Could be extended for 6 months before conversion)
Location: Minneapolis, MN (Remote/hybrid)
 
Role Objective
We are seeking a hands-on AI/ML Engineer to design, build, and deploy production-grade AI solutions. This role requires strong expertise in Generative AI, RAG (Retrieval-Augmented Generation), and enterprise integrations. The ideal candidate should be capable of independently delivering scalable AI systems aligned with business use cases.
 
Must-Have Skills (Non-Negotiable)
1. Core AI/ML Engineering
Strong proficiency in Python (NumPy, Pandas, PyTorch/TensorFlow)
Experience building and deploying end-to-end ML/AI systems
Ability to take solutions from prototype to production
2. Generative AI & LLMs
Hands-on experience with LLMs (OpenAI, Vertex AI, etc.)
Strong prompt engineering and evaluation techniques
Experience building enterprise-grade GenAI applications
3. RAG (Critical Requirement)
Proven experience designing and implementing RAG architectures
Experience with vector databases (Pinecone, Weaviate, etc.)
Ability to integrate domain-specific data into AI systems
4. Agentic AI / AI Agents
Experience building AI agents / multi-agent systems
Familiarity with orchestration frameworks and modern agent SDKs
5. API & Backend Development
Strong experience with FastAPI or Flask
Ability to build scalable AI services and APIs
6. Cloud & Deployment (GCP Preferred)
Experience with GCP (preferred) or AWS/Azure
Deploying AI solutions in cloud-native environments
Understanding of scalability, performance, and cost optimization
7. DevOps & Production Readiness
Experience with CI/CD (GitLab, Jenkins)
Infrastructure as Code (Terraform/Ansible)
Monitoring, logging, and AI observability
 
Nice-to-Have (Strong Plus)
Experience in education / digital learning platforms
Exposure to regulated environments
Knowledge of TypeScript / Java / SQL
Experience integrating AI into enterprise systems
 
Experience Required
5+ years in Software Engineering / AI/ML
Proven track record of:
Delivering production AI systems
Working in Agile cross-functional teams
Driving solutions with minimal oversight