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Retrieval Augmented Generation Jobs in Michigan (NOW HIRING)

cyberThink, Inc.

AI/ML Engineer

Ann Arbor, MI · On-site

Implement retrieval-augmented generation (RAG), semantic search, and knowledge retrieval solutions. * Build APIs, microservices, and backend services supporting AI workloads. * Evaluate, benchmark ...

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.

Amway Inc.

Sr AI Engineer

Ada, MI · On-site

$115K - $142K/yr

You'll work on agentic systems, retrieval-augmented generation (RAG) pipelines, and reusable AI services that power real business use cases. Depending on experience, this role may focus on owning ...

Amway

Sr AI Engineer

Ada, MI · On-site

$115K - $142K/yr

You'll work on agentic systems, retrieval-augmented generation (RAG) pipelines, and reusable AI services that power real business use cases. Depending on experience, this role may focus on owning ...

Optimal Inc.

Agentic AI Engineer

Warren, MI · On-site

Solid understanding of context handling, retrieval-augmented generation (RAG), and optimization techniques * Proficiency in Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow) * Experience ...

Optimal Inc.

Agentic AI Engineer

Warren, MI · On-site

Solid understanding of context handling, retrieval-augmented generation (RAG), and optimization techniques * Proficiency in Python and modern AI/ML frameworks (e.g., PyTorch, TensorFlow) * Experience ...

FastTek

Machine Learning Engineer

Dearborn, MI

$105K - $126K/yr

Strong understanding of Generative AI principles and architectures, including Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) systems. * Proven experience in building and ...

Stefanini

MLOps Engineer

Dearborn, MI · On-site

Develop and implement LLM-powered applications, including Retrieval-Augmented Generation (RAG), prompt orchestration, agentic workflows, and tool integrations. Create scalable APIs and AI services ...

Saanvi Technologies

Machine Learning Engineer 3

Dearborn, MI · On-site

$105K - $126K/yr

Develop and implement LLM-powered applications, including Retrieval-Augmented Generation (RAG), prompt orchestration, agentic workflows, and tool integrations. Create scalable APIs and AI services ...

Deloitte

Oracle Gen AI Manager

Detroit, MI · On-site

Architect and deliver integrated AI solutions, including agentic workflows, retrieval-augmented generation pipelines, and enterprise platform integrations * Define and enforce governance, security ...

General Motors

AI Agent Engineer

Warren, MI · On-site

Leverage LLMs and related AI services (e.g., retrieval-augmented generation, embeddings, vector search) to power agent capabilities. * Integrate agents with enterprise systems, APIs, and data sources ...

Orbotech

AI Software Engineer|Manufacturing

Ann Arbor, MI · On-site

$111K - $188K/yr

Implement retrieval-augmented generation (RAG) architectures using internal and external data sources * Design data pipelines and connectors to enterprise systems (e.g., databases, APIs, knowledge ...

Elara Caring

AI Automation Engineer II

Detroit, MI · On-site

Building or contributing to agentic workflows, retrieval-augmented generation (RAG), and LLM-powered automation solutions * Proficiency with integration and automation platforms (e.g., Logic Apps ...

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Retrieval Augmented Generation information

What does a retrieval augmented generation engineer do?

A Retrieval Augmented Generation engineer typically spends their day designing and implementing systems that combine information retrieval with advanced generative models, such as large language models. This includes fine-tuning models, integrating external data sources, developing vector search pipelines, and evaluating output quality. Collaboration with data scientists, machine learning engineers, and product teams is common to ensure the solutions meet user requirements and scale effectively. Additionally, RAG engineers often troubleshoot issues, monitor model performance in production, and stay informed about the latest advancements in AI and information retrieval.

What is a retrieval augmented generation?

A Retrieval Augmented Generation (RAG) job typically involves developing and optimizing AI systems that enhance text generation by incorporating external knowledge retrieved from relevant sources. Professionals in this field work on integrating retrieval mechanisms with large language models to improve the relevance, accuracy, and factual grounding of generated content. Common responsibilities include designing retrieval systems, fine-tuning language models, optimizing performance, and ensuring the seamless integration of factual data into AI-generated text. This role is highly interdisciplinary, involving expertise in natural language processing (NLP), machine learning, and information retrieval.

What skills and qualifications are needed for retrieval augmented generation?

To thrive in a Retrieval Augmented Generation (RAG) engineering role, you need a solid background in machine learning, natural language processing (NLP), and experience with scalable information retrieval systems, typically supported by a relevant degree in computer science or a related field. Familiarity with tools such as Python, PyTorch or TensorFlow, vector databases, and search platforms like Elasticsearch is essential, along with practical experience deploying and tuning RAG pipelines. Strong problem-solving skills, a collaborative mindset, and effective communication abilities set outstanding professionals apart in this field. These competencies are crucial for designing, implementing, and optimizing hybrid retrieval-generation AI systems that address complex, real-world information needs.

What are the most commonly searched types of Retrieval Augmented Generation jobs in Michigan? The most popular types of Retrieval Augmented Generation jobs in Michigan are:
What are popular job titles related to Retrieval Augmented Generation jobs in Michigan? For Retrieval Augmented Generation jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Retrieval Augmented Generation jobs in Michigan look for? The top searched job categories for Retrieval Augmented Generation jobs in Michigan are:
What cities in Michigan are hiring for Retrieval Augmented Generation jobs? Cities in Michigan with the most Retrieval Augmented Generation job openings:
Retrieval Augmented Generation jobs near you
Infographic showing various Retrieval Augmented Generation job openings in Michigan as of August 2026, with employment types broken down into 66% Full Time, 32% Part Time, and 2% Contract. Highlights an 62% Physical, 3% Hybrid, and 35% Remote job distribution.

AI/ML Engineer

cyberThink, Inc.

Ann Arbor, MI • On-site

Other

This job post has expired today. Applications are no longer accepted.

Job description

Job Description:

Mid-Level AI Engineer / AI Developer

Position Summary:

We are seeking a Mid-Level AI Engineer to design, develop, and deploy AI-powered applications and services. This role will work closely with software engineers, product managers, architects, and data teams to build scalable AI solutions using modern machine learning, generative AI, agent frameworks, and enterprise software engineering practices.

The ideal candidate combines strong software development skills with hands-on experience building, integrating, and operating AI solutions in production environments.

Key Responsibilities:

  • Design, develop, test, and deploy AI-enabled applications and services.
  • Build and integrate Generative AI solutions using large language models (LLMs).
  • Develop AI agents, workflows, prompts, tools, and orchestration frameworks.
  • Implement retrieval-augmented generation (RAG), semantic search, and knowledge retrieval solutions.
  • Build APIs, microservices, and backend services supporting AI workloads.
  • Evaluate, benchmark, and optimize AI model performance, quality, cost, and latency.
  • Develop automated testing, monitoring, and observability for AI applications.
  • Collaborate with product owners and business stakeholders to translate requirements into technical solutions.
  • Implement responsible AI, security, privacy, and governance controls within AI solutions.
  • Contribute to architectural decisions, coding standards, and engineering best practices.
  • Support AI applications in production and participate in troubleshooting and incident resolution.

Required Qualifications:

  • Bachelor's degree in Computer Science, Software Engineering, Data Science, or related field.
  • 3 6 years of software development experience.
  • 2+ years of experience building AI/ML or Generative AI applications.
  • Strong programming skills in one or more of:

o Python

o Java

o C#

o TypeScript/JavaScript

  • Experience developing REST APIs and distributed services.
  • Experience working with cloud platforms such as:

o Azure

o AWS

o Google Cloud Platform

  • Strong understanding of software engineering fundamentals:

o Design patterns

o Testing

o CI/CD

o Security

o Observability

Preferred Qualifications:

  • Experience with LLM platforms such as:

o OpenAI

o Anthropic Claude

o Azure OpenAI

o Gemini

  • Experience building:

o AI agents

o MCP-based integrations

o Tool calling frameworks

o Multi-agent workflows

  • Experience with RAG architectures and vector databases.
  • Familiarity with prompt engineering and evaluation frameworks.
  • Experience with containerization and orchestration:

o Docker

o Kubernetes

o Cloud Run

  • Experience working in Agile development environments.

Technical Skills:

AI / Machine Learning:

  • Generative AI
  • Large Language Models (LLMs)
  • Prompt Engineering
  • Retrieval-Augmented Generation (RAG)
  • Embeddings and Vector Search
  • AI Agent Frameworks
  • Model Evaluation and Benchmarking

Software Engineering:

  • Object-Oriented Design
  • API Design
  • Microservices
  • Event-Driven Architecture
  • Automated Testing
  • CI/CD Pipelines
  • Git and Source Control

Cloud & Infrastructure:

  • Azure, AWS, or Google Cloud Platform
  • Containers and Kubernetes
  • Monitoring and Observability
  • Identity and Access Management
  • Secure Deployment Practices

Success Measures:

  • Delivers production-ready AI features with high quality and reliability.
  • Improves developer and customer productivity through AI-enabled capabilities.
  • Builds scalable and secure AI services aligned with enterprise governance requirements.
  • Demonstrates ownership from design through deployment and production support.
  • Collaborates effectively across engineering, architecture, product, and business teams.

Typical Projects:

  • AI assistants and copilots
  • MCP servers and AI integrations
  • Knowledge search and RAG platforms
  • Agentic workflows and automation
  • Document intelligence solutions
  • AI-powered developer productivity tools
  • Conversational client experiences

This description is suitable for an Engineer III / Senior Associate level AI Developer someone who can independently deliver AI features while still working under technical guidance from senior engineers and architects

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