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Ai Rag Jobs in Michigan (NOW HIRING)

Beacon Specialized Living

AI/RPA Engineer

Kalamazoo, MI · On-site

$175K - $200K/yr

... RAG) using enterprise data • Model evaluation and drift monitoring • Deploy AI services using ... scalable cloud-native architecture (APIs, containers, CI/CD). • Optimize performance, cost, and ...

ALTEN Technology USA

Practice Manager - AI & Data

Troy, MI · On-site

$160K - $190K/yr

Generative AI & LLM ecosystems (prompt engineering, RAG, multi-agent systems) * Data Engineering & Modern Data Platforms (ETL/ELT, streaming, data lakes, data mesh) * Cloud-based AI architectures ...

Mastronardi Produce Limited

AI Engineer

Livonia, MI · On-site

Build retrieval-augmented generation (RAG) pipelines and knowledge bases that ground AI outputs in accurate, current company data. * Develop and refine prompts, system instructions, and evaluation ...

Motorola

Senior AI Engineer (US Remote)

Three Rivers, MI · Remote

$180K - $210K/yr

RAG (Retrieval-Augmented Generation) architectures * Agentic frameworks (LangChain, LlamaIndex, or AWS Bedrock Agents) Development Stack: * Python (AI/ML development & data processing) * NodeJS with ...

Judge Group, Inc.

Midlevel AI Developer

Ann Arbor, MI · On-site

$50 - $55/hr

Implement retrieval-augmented generation (RAG), semantic search, and knowledge retrieval solutions. Evaluate, benchmark, and optimize AI model performance, focusing on quality, cost, and latency.

Sunset Grown

AI Engineer

Livonia, MI · On-site

Build retrieval-augmented generation (RAG) pipelines and knowledge bases that ground AI outputs in accurate, current company data. * Develop and refine prompts, system instructions, and evaluation ...

Aquent

AI Software Developer

Ann Arbor, MI · On-site

$47.82 - $53.13/hr

Are you a passionate AI professional ready to transform the financial landscape with intelligent ... Implement retrieval-augmented generation (RAG), semantic search, and knowledge retrieval solutions ...

Corning

This role is focused on developing LLM-based applications, AI agents, RAG solutions, and automation pipelines that help analysts work faster, improve decision-making, and automate repetitive security ...

Deloitte

Google AI Architect

Detroit, MI

Build RAG and agentic solutions using Vertex AI Vector Search and BigQuery vector; implement context management, retrieval strategies, and observability. * Define end-to-end architectures across data ...

New

FTE Factory Advisors

AI Systems Engineer

Detroit, MI · On-site

Do you enjoy designing the systems behind AI agents, RAG applications, and data pipelines that run in real environments with data, security, and reliability constraints? If you're energized by ...

Aquent Talent

AI Software Developer [AQ-10880]

Ann Arbor, MI · On-site

$47.82 - $53.13/hr

Are you a passionate AI professional ready to transform the financial landscape with intelligent ... Implement retrieval-augmented generation (RAG), semantic search, and knowledge retrieval solutions ...

X by 2

Technical Lead (Applied AI)

Detroit, MI · On-site

Agentic SQL retrieval, MCP integration, agentic tool use, as well as vector databases & RAG ... AI Evaluation & Production Readiness : defining evaluation methods, testing model behavior ...

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Ai Rag information

What are the key skills and qualifications needed to thrive as an AI researcher?

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.

What is an AI RAG?

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.

What are common challenges faced by AI RAG 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 Michigan? For Ai Rag jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Ai Rag jobs in Michigan look for? The top searched job categories for Ai Rag jobs in Michigan are:
What cities in Michigan are hiring for Ai Rag jobs? Cities in Michigan with the most Ai Rag job openings:
Ai Rag jobs near you

AI/RPA Engineer

Beacon Specialized Living

Kalamazoo, MI • On-site

$175K - $200K/yr

Full-time

Posted 16 days ago

Beacon Specialized Living rating

5.5

Company rating: 5.5 out of 10

Based on 43 frontline employees who took The Breakroom Quiz

166th of 239 rated social care providers

Job description

Position Summary:
This role will be responsible for developing secure, compliant AI infrastructure and reusable frameworks that enable internal teams and external consultants to build and deploy AI agents for Operations, Human Resources, Admissions, and IT, while also supporting advanced LLM-driven clinical and client risk use cases integrated with Beacon's EHR, eMAR, HRIS, CRM, and incident management systems.
NOTE: **Applicants must be legally authorized to work in the United States**
Primary Responsibilities:
• Always be compliant with all company and regulatory policies and procedures.
• Design and maintain an enterprise AI Agent framework supporting:
• Task automation
• Data retrieval and summarization
• Workflow orchestration
• Human-in-the-loop approvals
• Build shared services including:
• Prompt management and versioning
• Tool and API integration layers
• Authentication, role-based access, and audit logging
Clinical AI & Client Risk Intelligence
• Develop and support LLM-powered clinical and risk-focused solutions such as:
• Behavioral and incident pattern analysis
• Medication adherence and documentation quality monitoring
• Early-warning indicators for client risk and escalation
• Integrate AI outputs into clinical workflows, dashboards, and alerts.
• Partner with clinical leadership to ensure interpretability and usability of AI insights.
LLM Engineering & MLOps
• Implement and manage LLM integrations including:
• Secure prompt pipelines
• Retrieval-Augmented Generation (RAG) using enterprise data
• Model evaluation and drift monitoring
• Deploy AI services using scalable cloud-native architecture (APIs, containers, CI/CD).
• Optimize performance, cost, and latency across production AI workloads.
Data Integration & Platform Collaboration
• Work with Data Engineering to leverage:
• Microsoft Fabric
• Azure Data Lake
• Power BI semantic models
• Integrate data from:
• EHR and eMAR platforms
Education and Qualifications:
• Bachelor's degree in Computer Science, Engineering, Data Science, or related field.
• 5+ years of experience in software engineering, data engineering, or AI engineering.
• Hands-on experience with:
• LLM APIs and orchestration frameworks
• Prompt engineering and RAG architectures
• API and microservice development

What Beacon Specialized Living employees say

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Hours and flexibility

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