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Remote Rag Jobs in Minneapolis, MN (NOW HIRING)

Noblesoft Technologies

AI/ML Engineer

Minneapolis, MN · Remote

$106K - $131K/yr

Minneapolis, MN(Remote) 1. Design, develop, test, document, and deploy Salesforce solutions based ... Familiarity with transformers, LLMs, and Retrieval-Augmented Generation (RAG) pipelines using ...

UnitedHealth Group

Data Scientist - Remote

Minnetonka, MN · On-site +1

$112K - $193K/yr

Building RAG pipelines * Implementing vector databases * Developing agentic workflows with LangChain * Integrating AI into enterprise applications for Claims Payment Integrity (PI) * The role ...

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How much do remote rag jobs pay per hour?

As of Jul 25, 2026, the average hourly pay for remote rag in Minneapolis, MN is $22.44, according to ZipRecruiter salary data. Most workers in this role earn between $18.80 and $23.85 per hour, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Remote Rag, and why are they important?

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What is a Remote RAG (Retrieval-Augmented Generation) specialist?

A Remote RAG specialist is a professional who works with Retrieval-Augmented Generation (RAG) systems, typically in the field of artificial intelligence and machine learning. RAG combines traditional information retrieval techniques with generative models like large language models to provide more accurate and contextually relevant answers to user queries. Remote RAG specialists often build, fine-tune, and maintain these systems while working from a remote location. They may also work on integrating RAG models into applications, improving retrieval accuracy, and customizing outputs based on user needs.

What are some common challenges faced by professionals working in a remote RAG (Responsible AI Governance) role?

Professionals in remote RAG roles often encounter challenges related to cross-functional collaboration and maintaining clear communication, especially when working across different time zones. Ensuring alignment on ethical AI standards and compliance requirements can be complex, as it typically involves coordinating with data scientists, legal teams, and business stakeholders. Staying current with evolving regulatory frameworks and best practices in AI governance is also essential, demanding continuous learning and adaptability. Building trust and rapport within a remote team can require extra effort, but leveraging digital collaboration tools and regular check-ins can help mitigate these challenges.
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Remote Rag jobs near you

Forward Deployment Engineer

Northern Base

Eden Prairie, MN • Remote

$100K - $120K/yr

Full-time

Posted 2 days ago

Job description

AI/ML & Forward Deployment Engineer | Remote | Full-time

Location: Eden Prairie, MN (Remote)
Experience: 8+ Years
Salary: $100,000 – $120,000 per year

We are seeking an experienced AI/ML & Forward Deployment Engineer to design, develop, and deploy production-grade AI and Machine Learning solutions. This role is ideal for professionals with expertise in Machine Learning, Generative AI, MLOps, and cloud-native application development who enjoy working directly with stakeholders to solve complex business problems.

Primary Skills

  • Python

  • Machine Learning

  • Deep Learning

  • Generative AI (GenAI)

  • Large Language Models (LLMs)

  • Retrieval-Augmented Generation (RAG)

  • LangChain / LlamaIndex

  • MLOps / LLMOps

  • Docker

  • Kubernetes

  • CI/CD

  • REST APIs / gRPC

  • AWS / Azure / GCP

  • Git

  • Data Engineering

Key Responsibilities

  • Collaborate with business stakeholders and customers to identify, define, and implement AI/ML use cases.

  • Design, develop, and deploy production-ready Machine Learning and Generative AI solutions.

  • Build and optimize RAG pipelines, embeddings, vector search, prompt engineering, and LLM-based applications.

  • Develop and maintain scalable ML models for classification, regression, forecasting, anomaly detection, and NLP.

  • Implement MLOps and LLMOps practices, including model versioning, deployment, monitoring, governance, and automated retraining.

  • Deploy AI services using Docker, Kubernetes, and CI/CD pipelines.

  • Develop secure integration services using REST APIs and gRPC.

  • Collaborate with Data Engineers to build reliable and scalable data pipelines.

  • Monitor model performance, detect drift, troubleshoot production issues, and optimize system performance.

  • Mentor engineering teams and contribute to architecture reviews, technical documentation, and reusable engineering frameworks.

Required Qualifications

  • 8+ years of software engineering or AI/ML engineering experience.

  • Strong proficiency in Python programming.

  • Hands-on experience with Machine Learning and Deep Learning frameworks.

  • Experience building Generative AI and Large Language Model (LLM) applications.

  • Practical experience with Retrieval-Augmented Generation (RAG) architectures.

  • Knowledge of LangChain, LlamaIndex, vector databases, and prompt engineering.

  • Experience with Docker, Kubernetes, and cloud platforms (AWS, Azure, or GCP).

  • Experience implementing MLOps/LLMOps pipelines and CI/CD.

  • Strong understanding of REST APIs, microservices, and distributed systems.

  • Excellent analytical, communication, and stakeholder management skills.

Preferred Qualifications

  • Experience with model monitoring, observability, and governance.

  • Familiarity with experimentation frameworks, A/B testing, and evaluation metrics.

  • Experience working in customer-facing or consulting environments.

  • Knowledge of secure AI deployments, RBAC, encryption, and compliance best practices.

If you are passionate about building scalable AI solutions and delivering business impact through modern Machine Learning and Generative AI technologies, we encourage you to apply.

To Apply

Email: numa.a@northern-base.com
Phone: 571-512-7622

Keywords: AI Engineer, Machine Learning Engineer, Forward Deployment Engineer, Generative AI Engineer, LLM Engineer, Python Developer, MLOps Engineer, LLMOps, LangChain, LlamaIndex, RAG, NLP, Deep Learning, Docker, Kubernetes, Cloud Engineer, AWS, Azure, GCP, Artificial Intelligence, GenAI.