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Mlops Machine Learning Engineer Jobs in Michigan

Ascentt

Senior Machine Learning Engineer

Ann Arbor, MI

$102K - $140K/yr

Senior Machine Learning Engineer Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. We turn enterprise data into real-time decisions ...

FastTek

Machine Learning Engineer

Dearborn, MI

$105K - $126K/yr

Machine Learning Engineer #1054987 * Employees in this job function are responsible for designing ... CI/CD/CT, MLOps, and LLMOps -- including guardrail integration, prompt versioning, and ...

Stefanini Group

MLOps Engineer

Dearborn, MI · On-site

Stefanini is looking for a MLOps Engineer (Dearborn, MI) For quick apply, please reach out to ... Design, develop, and deploy machine learning models, including predictive, optimization, and ...

Stefanini Group

MLOps Engineer

Dearborn, MI · On-site

Stefanini is looking for a MLOps Engineer (Dearborn, MI) For quick apply, please reach out to ... Design, develop, and deploy machine learning models, including predictive, optimization, and ...

Stefanini Group

MLOps Engineer

Dearborn, MI · On-site

Stefanini is looking for a MLOps Engineer (Dearborn, MI) For quick apply, please reach out to ... Design, develop, and deploy machine learning models, including predictive, optimization, and ...

Stefanini

MLOps Engineer

Dearborn, MI · On-site

Stefanini is looking for a MLOps Engineer (Dearborn, MI) For quick apply, please reach out to ... Responsibilities Design, develop, and deploy machine learning models, including predictive ...

Indeed

Senior Machine Learning Engineer

Detroit, MI

$95K - $131K/yr

Comscore, Total Visits, March 2025) Day to Day As a Senior Machine Learning Engineer on our Sourcing team, you will work on developing and deploying ML and AI solutions in production. You'll be ...

General Motors

Senior Machine Learning Engineer

Warren, MI · On-site +1

$222K - $227K/yr

Machine Learning Frameworks, including TensorFlow and PyTorch; Mathematical Reasoning and Probability; Programming in C++ or Python; Experience with Robot Operating System (ROS), OpenCV, or PCL;

Autodesk

Senior Machine Learning Test Engineer

Three Rivers, MI · On-site +1

$101K - $132K/yr

Job Requisition ID # 26WD98377 Senior Machine Learning Test Engineer Location: United States East ... Your skills span test strategy, automation, and a little MLOps, with a strong software engineering ...

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All JobsMlops Machine Learning Engineer JobsMlops Machine Learning Engineer Jobs in Michigan

Mlops Machine Learning Engineer information

Is MLOps harder than DevOps?

MLOps, as a specialized subset of DevOps focused on deploying and maintaining machine learning models, often involves additional challenges such as data management, model versioning, and monitoring. While both require skills in automation, scripting, and cloud environments, MLOps typically demands expertise in machine learning workflows and tools like TensorFlow or PyTorch, making it more complex in certain aspects compared to traditional DevOps.

What does an MLOps Machine Learning Engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

How does an MLOps Machine Learning Engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

Are MLOps engineers in demand?

MLOps engineers are in high demand due to the increasing adoption of machine learning models in various industries. Their skills in deploying, managing, and scaling machine learning systems, along with knowledge of tools like Docker, Kubernetes, and cloud platforms, make them valuable in the job market.

What engineers make $500,000?

Senior machine learning engineers, including those specializing in MLOps, often reach or exceed $500,000 annually with experience, advanced skills, and in high-demand industries like tech or finance. Compensation can include base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

How much do MLOps engineers make?

MLOps engineers typically earn between $100,000 and $150,000 annually, with salaries increasing based on experience, location, and expertise in tools like Kubernetes, Docker, and cloud platforms. Senior roles or those with specialized skills can exceed $180,000 per year.

What are the key skills and qualifications needed to thrive as an MLOps Machine Learning Engineer, and why are they important?

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.
What are popular job titles related to Mlops Machine Learning Engineer jobs in Michigan? For Mlops Machine Learning Engineer jobs in Michigan, the most frequently searched job titles are:
What job categories do people searching Mlops Machine Learning Engineer jobs in Michigan look for? The top searched job categories for Mlops Machine Learning Engineer jobs in Michigan are:
What cities in Michigan are hiring for Mlops Machine Learning Engineer jobs? Cities in Michigan with the most Mlops Machine Learning Engineer job openings:
Mlops Machine Learning Engineer jobs near you
Senior Machine Learning Engineer

Senior Machine Learning Engineer

Ascentt

Ann Arbor, MI

$102K - $140K/yr

Other

Posted 20 days ago

Job description

Senior Machine Learning Engineer

Ascentt is building cutting-edge data analytics & AI/ML solutions for global automotive and manufacturing leaders. We turn enterprise data into real-time decisions using advanced machine learning and GenAI. Our team solves hard engineering problems at scale, with real-world industry impact. We're hiring passionate builders to shape the future of industrial intelligence.

Location: Ann Arbor, Michigan Experience Level: 7+ Years Department: Data Science / Engineering Employment Type: Full-time

About the Role:

We are looking for an experienced Senior Machine Learning Engineer with deep expertise in statistical and machine learning techniques, large-scale data processing, and model deployment in cloud environments. The ideal candidate will be a self-starter with strong problem-solving skills and hands-on experience in building and deploying ML models using big data technologies like PySpark and cloud platforms like Amazon SageMaker.

Key Responsibilities:
  • Design, develop, and deploy scalable machine learning models for real-world business problems using structured and unstructured data.
  • Analyze large datasets using PySpark and other distributed computing frameworks to extract insights and prepare features for ML pipelines.
  • Apply a wide range of statistical, machine learning, and deep learning techniques, including but not limited to regression, classification, clustering, time-series forecasting, and NLP.
  • Own end-to-end ML pipelines from data ingestion, preprocessing, training, validation, tuning, and deployment.
  • Utilize Amazon SageMaker or similar platforms for building, training, and deploying models in a production-grade environment.
  • Collaborate closely with data engineers, data scientists, and product teams to integrate models with business workflows.
  • Monitor and improve model performance, scalability, and reliability in production.
  • Contribute to setting up and maintaining the ML environment and tooling (including environment configuration, CI/CD pipelines for ML, model versioning, etc.).
Required Qualifications:
  • 7+ years of experience in machine learning, data science, or related fields.
  • Strong programming skills in Python with experience in ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
  • Hands-on experience with PySpark for big data processing and model development.
  • Proficient in building models on large-scale datasets (terabytes to petabytes).
  • Solid understanding of statistical analysis, probability, hypothesis testing, and experimental design.
  • Experience with Amazon SageMaker (or similar cloud-based ML platforms).
  • Strong knowledge of ML Ops practices including version control, model monitoring, and retraining strategies.
  • Familiarity with containerization (Docker) and CI/CD practices for ML projects is a plus.
  • Excellent communication skills and the ability to clearly explain complex concepts to non-technical stakeholders.
Preferred Qualifications:
  • Master's or Ph.D. in Computer Science, Statistics, Mathematics, or a related quantitative discipline.
  • Experience with workflow orchestration tools (e.g., Airflow, Kubeflow).
  • Prior experience in domains like Manufacturing, finance, healthcare, or e-commerce is a plus.

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