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Mlops Jobs in Michigan (NOW HIRING)

Stefanini

MLOps Engineer

Dearborn, MI · On-site

Stefanini is looking for a MLOps Engineer (Dearborn, MI) For quick apply, please reach out to Navneet Pathak at / We are seeking an experienced AI Engineer to design, develop, and deploy intelligent ...

Huntington

MLOps Automation Senior Lead Engineer

Detroit, MI · On-site +1

$93K - $189K/yr

  • Medical

  • Life

  • Retirement

  • PTO

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and deploying MLOps Automation for some of Huntington's most valuable and most challenging data-driven projects.

HARMAN International

Principal Engineer, AI

Novi, MI · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Define and enforce MLOps standards using MLflow, including: * Experiment tracking * Model versioning and registry * Promotion workflows (Dev → QA → Prod) * Co-design CI/CD pipelines with Platform ...

Saanvi Technologies

Machine Learning Engineer 3

Dearborn, MI · On-site

$105K - $126K/yr

This role combines expertise in Data Science, Software Engineering, and MLOps to deliver scalable, production-ready AI systems that generate measurable business value. The ideal candidate will have ...

FastTek

Machine Learning Engineer

Dearborn, MI · On-site

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Establish and maintain MLOps pipelines including automated training, deployment, monitoring, and retraining * Ensure AI solutions are scalable, secure, reliable, and production-ready * Collaborate ...

Corning

$95K - $130K/yr

You will help translate data science prototypes into secure, scalable, and production-ready solutions while influencing architecture, MLOps practices, and technical standards. This is an individual ...

Dow - THE DOW CHEMICAL COMPANY

Machine Learning Engineer

Midland, MI · On-site

$98K - $118K/yr

  • Medical

  • Life

  • Retirement

  • PTO

You will also help drive a culture around setting standards and adopting best practices for machine learning and MLOps across the organization. Responsibilities * Designs and implements pipelines and ...

Whisker

Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Develops and supports MLOps workflows, including model tracking, versioning, and lifecycle management using MLflow * Builds and maintains CI/CD pipelines for both data engineering and machine ...

Whisker

Data Engineer

Auburn Hills, MI · On-site

$108K - $130K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

Develops and supports MLOps workflows, including model tracking, versioning, and lifecycle management using MLflow * Builds and maintains CI/CD pipelines for both data engineering and machine ...

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All JobsMlops JobsMlops Jobs in Michigan

Mlops information

See Michigan salary details

$89.1K

$139.9K

$166.4K

How much do mlops jobs pay per year?

As of Aug 13, 2026, the average yearly pay for mlops in Michigan is $139,881.00, according to ZipRecruiter salary data. Most workers in this role earn between $132,165.00 and $151,924.00 per year, depending on experience, location, and employer.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

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

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.
What are the most commonly searched types of Mlops jobs in Michigan? The most popular types of Mlops jobs in Michigan are:
What are popular job titles related to Mlops jobs in Michigan? For Mlops jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Mlops jobs in Michigan look for? The top searched job categories for Mlops jobs in Michigan are:
What cities in Michigan are hiring for Mlops jobs? Cities in Michigan with the most Mlops job openings:
Mlops jobs near you
Infographic showing various Mlops job openings in Michigan as of August 2026, with employment types broken down into 88% Full Time, 4% Part Time, 1% Temporary, and 7% Contract. Highlights an 70% Physical, 10% Hybrid, and 20% Remote job distribution, with an average salary of $139,881 per year, or $67.3 per hour.

MLOps Engineer

Stefanini

Dearborn, MI • On-site

Other

Re-posted yesterday

Job description


Stefanini Group is hiring!
Stefanini is looking for a MLOps Engineer (Dearborn, MI)
For quick apply, please reach out to Navneet Pathak at /
We are seeking an experienced AI Engineer to design, develop, and deploy intelligent solutions that leverage Machine Learning, Large Language Models (LLMs), and emerging Agentic AI capabilities to transform business processes and drive operational efficiency. The ideal candidate will have hands-on experience building and operationalizing AI/ML solutions in enterprise environments, with a strong focus on Generative AI, intelligent automation, and cloud-native architectures.
Responsibilities Design, develop, and deploy machine learning models, including predictive, optimization, and Generative AI solutions. Build end-to-end AI workflows encompassing data ingestion, feature engineering, model training, deployment, monitoring, and continuous improvement. Develop and implement LLM-powered applications, including Retrieval-Augmented Generation (RAG), prompt orchestration, agentic workflows, and tool integrations. Create scalable APIs and AI services that seamlessly integrate with enterprise applications and business processes. Establish and maintain MLOps practices, including automated training, deployment, monitoring, retraining, and performance management. Ensure AI solutions are reliable, scalable, secure, and optimized for production environments.
Skills RequiredPython, Machine Learning, Data Science, Google Cloud Platform, Big QueryPython (advanced), SQL Machine Learning & Deep Learning LLMs, Prompt Engineering, RAG, Embeddings Agentic AI / AI Agents / Tool Calling Vector Databases ML Frameworks: Scikit-learn, TensorFlow, PyTorch MLOps: MLflow, Airflow, CI/CD, model deployment & monitoring Cloud: AWS or Google Cloud Platform Docker, Kubernetes API development (FastAPI / Flask) Data pipelines (ETL), data lakes/warehouses Strong system design & production AI experience
Experience Required6+ years of experience in IT; 4+ years in development Experience designing and implementing Agentic AI solutions, multi-step workflows, autonomous agents, and tool-calling architectures.Proficient with AI orchestration frameworks such as LangChain, LlamaIndex, CrewAI, AutoGen, and similar technologies.Hands-on experience with MLOps tools including MLflow, Airflow, Vertex AI, SageMaker, and Kubeflow.Expertise in containerization and orchestration technologies such as Docker and Kubernetes.Familiarity with vector databases, embeddings, Retrieval-Augmented Generation (RAG), and semantic search architectures.Strong programming experience in Python, including backend development, API design, automation, and software engineering best practices.Experience building, deploying, and supporting machine learning models in production environments with frameworks like Scikit-learn, TensorFlow, and PyTorch.Practical experience developing applications using Large Language Models (LLMs), prompt engineering, and Generative AI technologies.Experience building AI solutions on cloud platforms such as Google Cloud Platform and AWS.Strong understanding of the software development lifecycle, version control, testing, and deployment practices.Experience working with enterprise-scale data environments, data lakes, and optimizing AI systems for scalability, performance, reliability, and cost efficiency.Experience building AI-powered products, dashboards, analytics solutions, or intelligent automation platforms.
Education RequiredBachelor's Degree
Education PreferredMaster's degree
**Listed salary ranges may vary based on experience, qualifications, and local market. Also, some positions may include bonuses or other incentives***
Stefanini takes pride in hiring top talent and developing relationships with our future employees. Our talent acquisition teams will never make an offer of employment without having a phone conversation with you. Those face-to-face conversations will involve a description of the job for which you have applied. We will also speak with you about the process, including interviews and job offers.
About Stefanini Group
The Stefanini Group is a global provider of offshore, onshore and near shore outsourcing, IT digital consulting, systems integration, application, and strategic staffing services to Fortune 1000 enterprises around the world. Our presence is in countries like the Americas, Europe, Africa, and Asia, and more than four hundred clients across a broad spectrum of markets, including financial services, manufacturing, telecommunications, chemical services, technology, public sector, and utilities. Stefanini is a CMM level 5, IT consulting company with a global presence. We are a CMM Level 5 company.
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