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Ai Devops Engineer Jobs in Michigan (NOW HIRING)

Steelcase Inc

Operations Engineer

Grand Rapids, MI · On-site

$66K - $90K/yr

Minimum Qualifications: • 3+ years of experience in manufacturing, operations engineering, industrial engineering, continuous improvement, or a related field. • Experience analyzing operational ...

New

Tech Tammina LLC

AI Engineer

Okemos, MI · On-site

... AI Developer Okemos, MI (Hybrid): Minimum of 3 days per week onsite in Okemos. or 100% Remote: Open only to Senior-level candidates located in Eastern Time (EST). Must Have Technical Skills: * Python ...

Steelcase, Inc.

Operations Engineer

Grand Rapids, MI · On-site

$66K - $90K/yr

Minimum Qualifications: • 3+ years of experience in manufacturing, operations engineering, industrial engineering, continuous improvement, or a related field. • Experience analyzing operational ...

New

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Ai Devops Engineer information

See Michigan salary details

$14

$52

$75

How much do ai devops engineer jobs pay per hour?

As of Sep 5, 2026, the average hourly pay for ai devops engineer in Michigan is $52.76, according to ZipRecruiter salary data. Most workers in this role earn between $44.23 and $60.53 per hour, depending on experience, location, and employer.

What is an AI DevOps engineer?

AI DevOps Engineers are professionals who blend expertise in artificial intelligence (AI) and DevOps practices to streamline the development, deployment, and maintenance of AI-powered applications. They automate and manage the continuous integration and continuous deployment (CI/CD) pipelines for machine learning models, ensuring scalability, reliability, and efficiency in production environments. Their role also includes monitoring AI systems, managing model versions, and collaborating with data scientists, software engineers, and IT teams to bridge the gap between development and operations. This position is crucial for organizations looking to operationalize AI solutions at scale.

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

To thrive as an AI DevOps Engineer, you need expertise in software engineering, machine learning pipelines, and cloud infrastructure, often backed by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD systems, version control (e.g., Git), and cloud platforms (AWS, Azure, GCP), as well as certifications such as AWS Certified DevOps Engineer, are highly valuable. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and adapt to evolving project requirements. These competencies are essential to ensure robust, scalable, and efficient deployment of AI solutions in production environments.

How does an AI DevOps engineer typically collaborate with data scientists and software engineers on AI projects?

An AI DevOps Engineer plays a crucial role in bridging the gap between data scientists and software engineers by streamlining the deployment and monitoring of AI models. They often work closely with data scientists to automate model training, testing, and deployment pipelines, ensuring that models move smoothly from development to production. Additionally, they collaborate with software engineers to integrate models into scalable applications, manage infrastructure, and set up monitoring systems to track performance and reliability. This role requires strong communication skills and a proactive approach to troubleshooting issues that arise in cross-functional teams.

What is the difference between Ai Devops Engineer vs Data Engineer?

AspectAi Devops EngineerData Engineer
Required CredentialsCertifications in cloud platforms, DevOps tools, AI/ML frameworksCertifications in data management, SQL, cloud platforms
Work EnvironmentCollaborates with AI/ML teams, DevOps, cloud infrastructureWorks with data pipelines, databases, big data tools
Employer & Industry UsageTech companies, AI startups, cloud service providersFinance, healthcare, e-commerce, data-driven industries

Ai Devops Engineers focus on deploying and maintaining AI/ML models in production using DevOps practices, while Data Engineers build and manage data pipelines and infrastructure. Both roles require cloud and scripting skills but serve different stages of data and AI workflows.

Will AI take AI Devops Engineer jobs?

AI DevOps Engineers focus on integrating AI models into deployment pipelines, automating infrastructure, and managing AI systems. While AI tools can automate certain tasks, the role requires expertise in both AI and DevOps practices, making complete automation unlikely in the near term. Human oversight remains essential for designing, maintaining, and improving AI deployment processes.

What are popular job titles related to Ai Devops Engineer jobs in Michigan?

For Ai Devops Engineer jobs in Michigan, the most frequently searched job titles are:

What job categories do people searching Ai Devops Engineer jobs in Michigan look for?

The top searched job categories for Ai Devops Engineer jobs in Michigan are:

Ai Devops Engineer jobs near you
Infographic showing various Ai Devops Engineer job openings in Michigan as of August 2026, with employment types broken down into 74% Full Time, 20% Part Time, 5% Contract, and 1% Nights. Highlights an 66% Physical, 4% Hybrid, and 30% Remote job distribution, with an average salary of $109,741 per year, or $52.8 per hour.

AI Engineer / Applied AI Developer

Tech Tammina LLC

Okemos, MI • On-site

Contractor

Re-posted 20 days ago

Job description

Role: AI Engineer / Applied AI Developer

Location: Okemos, MI or Farmington Hills, MI (3 days in a week onsite)

Duration: Long term

Rate: Market

Summary:

·         The ideal candidate should have strong hands-on development skills, with a passion for building AI-powered proof-of-concepts and exploring cutting-edge AI capabilities. 

·         The candidate must be comfortable working in a fast-paced, research-driven environment, be a problem solver with excellent critical thinking, and have a strong foundation in Python programming.

·         Experience with large language models is a must.

·         Machine learning frameworks, or AI cloud services is a plus but not required.

Must Have Technical Skills:

•        Java (advanced proficiency)

•        Python (intermediate to advanced proficiency)

•        Ability to quickly build working proof-of-concepts

•        Strong analytical and critical thinking skills

•        Experience with prompt engineering and function calling in LLMs

•        Experience integrating APIs and handling structured/unstructured data

•        Familiarity with Git and development in a collaborative environment