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Remote Retrieval Augmented Generation Jobs in Rosemount, MN

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 ...

FAVARH

AI Principal Engineer

Minneapolis, MN · On-site +1

This role is fully remote, with no regular in-office requirement. The Contributions You'll Make ... Experience with retrieval-augmented generation (RAG), vector databases, and AI orchestration ...

UnitedHealth Group

Data Scientist - Remote

Minnetonka, MN · On-site +1

$112K - $193K/yr

Support Retrieval-Augmented Generation (RAG) solutions, including semantic search, vector databases, and LLM integration * Help design and implement AI-powered enterprise applications * Analyze large ...

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Remote Retrieval Augmented Generation information

What skills and qualifications are needed to thrive as a remote retrieval augmented generation engineer?

To thrive as a Remote Retrieval Augmented Generation (RAG) Engineer, you need a strong background in machine learning, natural language processing, and information retrieval, often backed by a degree in computer science or a related field. Familiarity with tools and frameworks like PyTorch, TensorFlow, Hugging Face Transformers, and experience with retrieval systems such as Elasticsearch or FAISS are typically required. Problem-solving, effective communication, and adaptability are important soft skills for collaborating remotely and iterating on rapidly evolving AI solutions. These skills ensure the engineer can design, deploy, and optimize robust RAG systems that effectively combine retrieval and generation for high-quality AI outputs.

What is the difference between Remote Retrieval Augmented Generation vs Remote Data Scientist?

AspectRemote Retrieval Augmented GenerationRemote Data Scientist
CredentialsAI/ML knowledge, programming skillsStatistics, programming, domain expertise
Work EnvironmentAI development, NLP projectsData analysis, model building
Industry UsageAI, NLP, machine learningTech, finance, healthcare
Search & ComparisonOften compared for AI roles involving language modelsCompared for data analysis roles

Remote Retrieval Augmented Generation focuses on developing AI models that combine retrieval techniques with language generation, requiring expertise in AI, NLP, and programming. Remote Data Scientists analyze data, build models, and interpret results, often with statistical and domain knowledge. While both roles may work remotely and involve data handling, Retrieval Augmented Generation emphasizes AI model development, whereas Data Scientists focus on data analysis and insights.

What are common challenges faced by professionals working in remote retrieval augmented generation roles, and how can they be addressed?

Professionals in Remote Retrieval Augmented Generation (RAG) roles often encounter challenges related to integrating diverse data sources, ensuring low latency in information retrieval, and maintaining the quality and relevance of augmented outputs. Coordinating effectively with distributed teams and adapting to rapidly evolving AI technologies are also common hurdles. To address these, staying current with best practices in data engineering, leveraging robust APIs, and participating in regular team check-ins can help ensure smooth collaboration and system performance.

What is remote retrieval augmented generation?

Remote Retrieval Augmented Generation (RAG) is an advanced AI technique that combines large language models with external information sources. In a remote RAG setup, the model retrieves relevant data from remote databases or APIs during the generation process, enhancing its responses with up-to-date or domain-specific knowledge. This approach is widely used in applications that require accurate, context-aware answers, such as chatbots, search engines, and virtual assistants. By leveraging remote retrieval, RAG systems can access a broader range of information without needing to store all data locally.
Remote Retrieval Augmented Generation jobs near you

AI/ML Engineer

Noblesoft Technologies

Minneapolis, MN • Remote

$106K - $131K/yr

Contractor

Re-posted 20 days ago

Job description

Job Title: AI/ML Engineer
Location: Minneapolis, MN(Remote)
Job Description:    
1.    Design, develop, test, document, and deploy Salesforce solutions based on business needs.
2.    Develop and deploy AI/ML models for real-time decision-making and automation.
3.    Integrate AI/ML solutions into Salesforce CRM to enable intelligent data retrieval, personalized recommendations, workflow automation, forecasting, scoring, and opportunity insights.
4.    Enhance Salesforce applications with advanced AI features using both native (Einstein/Einstein GPT) and external technologies (Python-based models or Azure/AWS ML services).
5.    Build and optimize Retrieval-Augmented Generation (RAG) pipelines using vector databases and Large Language Models (LLMs) for improved contextual understanding within Salesforce workflows.
6.    Extend platform functionality using Apex (Triggers/Classes), LWC, Aura Framework, Visualforce Pages, Apex APIs and web services.
MUST HAVE SKILLS    
1.    Bachelor's degree in Computer Science, Information Systems, Statistics, or a comparable discipline is required, with prior experience in data analysis or a related field being advantageous
2.    5-7 years of experience in Power BI development and implementation
3.    AI/ML Expertise: Building and deploying models for real-time decision-making and automation.
4.    Integration Skills: AI/ML integration with Salesforce CRM (Einstein/Einstein GPT and external technologies like Python-based models or Azure/AWS ML services).
5.    Generative AI Knowledge: Familiarity with transformers, LLMs, and Retrieval-Augmented Generation (RAG) pipelines using vector databases.
6.    Automation Development: Creating AI-powered automation solutions, including Einstein Bots and custom bots for sales/service workflows.
7.    CI/CD Proficiency: Managing deployment processes using Git.
8.    Cloud Platforms: Experience with Azure/AWS ML services and enterprise-grade integrations.
9.    Security & Compliance: Ensuring data privacy, scalability, and reliability of AI models in production.
10.    Collaboration: Ability to work with product managers, engineers, and data teams for AI-driven enhancements.
11.    Continuous Improvement: Monitoring model accuracy and implementing feedback loops for better user experience