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Ai Rag Jobs in Wisconsin (NOW HIRING)

Rivi Consulting Group

Python AI Developer

Green Bay, WI · On-site

$49 - $67.25/hr

This individual will not just be wrapping APIs; they will be building the memory layers, RAG pipelines, and ontological structures that allow our AI to serve as a true co-pilot for logistics ...

New

AllSTEM Connections

Principal AI Engineer

Milwaukee, WI · On-site

$197K - $208K/yr

Principal AI Systems Architect (Contract Engagement) Role Overview We are seeking a Principal AI ... Architect production-scale RAG (Retrieval-Augmented Generation) pipelines, vector database ...

New

Qualysoft

RAG-Applikationen) in Microsoft Azure Aufbau, Orchestrierung und Weiterentwicklung von AI-Agenten und Multi-Agenten-Systemen Einsatz und Integration von Model Context Protocols (MCPs) Aufnahme ...

Genesys

Experience building RAG-based systems, vector databases, and semantic search architectures. * Demonstrated ability to lead large-scale AI initiatives and influence technical strategy. * Deep ...

Genesys

Experience building RAG-based systems, vector databases, and semantic search architectures. * Demonstrated ability to lead large-scale AI initiatives and influence technical strategy. * Deep ...

Genesis10

Generative AI Engineer - Remote

Green Bay, WI · On-site

... RAG architectures, Vector databases, Prompt engineering, and LLM orchestration • Strong AWS experience including Lambda, S3, DynamoDB, EC2, Fargate, and Bedrock • Experience developing APIs using ...

Harris

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal ... Identify and implement AI agents and automation tools that reduce manual effort at each stage of ...

Cayenta

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal ... Identify and implement AI agents and automation tools that reduce manual effort at each stage of ...

Harris

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal ... Identify and implement AI agents and automation tools that reduce manual effort at each stage of ...

Harris

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal ... Identify and implement AI agents and automation tools that reduce manual effort at each stage of ...

Harris

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal ... Identify and implement AI agents and automation tools that reduce manual effort at each stage of ...

Cayenta

Operate and continuously improve a retrieval-augmented generation (RAG) pipeline for proposal ... Identify and implement AI agents and automation tools that reduce manual effort at each stage of ...

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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 Wisconsin? For Ai Rag jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Ai Rag jobs in Wisconsin look for? The top searched job categories for Ai Rag jobs in Wisconsin are:
What cities in Wisconsin are hiring for Ai Rag jobs? Cities in Wisconsin with the most Ai Rag job openings:
Ai Rag jobs near you
Infographic showing various Ai Rag job openings in Wisconsin as of July 2026, with employment types broken down into 92% Full Time, and 8% Part Time. Highlights an 83% In-person, and 17% Hybrid job distribution.
Python AI Developer

Python AI Developer

Rivi Consulting Group

Green Bay, WI • On-site

$49 - $67.25/hr

Other

Posted yesterday

New

Job description

Overview

We are looking for an engineer to help scale our backend infrastructure and deepen our agentic capabilities. This individual will not just be wrapping APIs; they will be building the memory layers, RAG pipelines, and ontological structures that allow our AI to serve as a true co-pilot for logistics professionals.

Technical Requirements

- Expert Python Engineering: Production-grade, asynchronous Python (FastAPI/Pydantic) with a focus on performance and scalability.

- Agentic Frameworks & Reasoning: Hands-on experience with LangGraph, DSPy, or LangChain. Must understand state management, agent loops, and reliable tool-calling. Experience with evaluations is a significant bonus.

- Knowledge Graphs & Ontologies: Experience building or working with ontologies.

- Advanced Memory Management: Implementation of "long-term memory" using vector databases and schemas to maintain context over long horizons.

- RAG & LLMs: Experience with RAG optimization (reranking, query transformation) and/or fine-tuning LLMs for domain-specific reasoning.

- Experience with Google Cloud Platform is preferred

- Understands Secure SDLC practices

Core Competencies

- Quick learner with high agency.

- A "product engineer" mindset.

- Comfort with ambiguity and a sense of extreme ownership.

- Experience working in a fast paced environment.

Apply