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Ai Rag Jobs in Mattawan, MI (NOW HIRING)

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 ...

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

Ai Rag information

See Mattawan, MI salary details

$31.1K

$56.6K

$81.1K

How much do ai rag jobs pay per year?

As of Aug 9, 2026, the average yearly pay for ai rag in Mattawan, MI is $56,599.00, according to ZipRecruiter salary data. Most workers in this role earn between $47,600.00 and $63,200.00 per year, depending on experience, location, and employer.

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 cities near Mattawan, MI are hiring for Ai Rag jobs? Cities near Mattawan, MI with the most Ai Rag job openings:

$175K - $200K/yr

Full-time

Posted 15 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

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