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Mlops Jobs in Wisconsin (NOW HIRING)

Techvilla Solutions

ML Ops Engineer

North Lake, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Brown Deer, WI

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Hubertus, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Nashotah, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

West Allis, WI

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Butler, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Elm Grove, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Franklin, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Big Bend, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Wales, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Milwaukee, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Waukesha, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Lannon, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Menomonee Falls, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Greenfield, WI

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Richfield, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Wauwatosa, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Brookfield, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

Techvilla Solutions

ML Ops Engineer

Vernon, WI · On-site

Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining. * Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration. * Deploy and ...

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Showing results 21-40

All JobsMlops JobsMlops Jobs in Wisconsin

Mlops information

See Wisconsin salary details

$92.7K

$145.4K

$173K

How much do mlops jobs pay per year?

As of Sep 6, 2026, the average yearly pay for mlops in Wisconsin is $145,450.00, according to ZipRecruiter salary data. Most workers in this role earn between $137,426.00 and $157,972.00 per year, depending on experience, location, and employer.

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 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 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.

Is MLOps outdated?

MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.

What is the average salary in MLOps?

The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.

What are the most commonly searched types of Mlops jobs in Wisconsin?

The most popular types of Mlops jobs in Wisconsin are:

What are popular job titles related to Mlops jobs in Wisconsin?

For Mlops jobs in Wisconsin, the most frequently searched job titles are:

What job categories do people searching Mlops jobs in Wisconsin look for?

The top searched job categories for Mlops jobs in Wisconsin are:

What cities in Wisconsin are hiring for Mlops jobs?

Cities in Wisconsin with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Wisconsin as of August 2026, with employment types broken down into 93% Full Time, 5% Part Time, and 2% Contract. Highlights an 79% Physical, 6% Hybrid, and 15% Remote job distribution, with an average salary of $145,450 per year, or $69.9 per hour.

ML Ops Engineer

Techvilla Solutions

North Lake, WI • On-site

Full-time

Posted 17 days ago

Job description

We are looking for an experienced ML Ops Engineer to build, automate, and maintain scalable machine learning infrastructure and deployment pipelines. The ideal candidate will have strong experience with cloud platforms, CI/CD, containerization, model deployment, monitoring, and ML lifecycle management.

Roles and Responsibilities
  • Build and maintain MLOps pipelines for model development, deployment, monitoring, and retraining.
  • Automate ML workflows using CI/CD, infrastructure as code, and workflow orchestration.
  • Deploy and manage machine learning models across cloud and on-premise environments.
  • Implement model versioning, experiment tracking, feature management, and model governance.
  • Build scalable infrastructure using Docker, Kubernetes, and cloud services.
  • Monitor model performance, data quality, system health, and production workloads.
  • Collaborate with Data Scientists, ML Engineers, Data Engineers, and DevOps teams.
  • Troubleshoot production ML systems and optimize reliability, scalability, and performance.
  • Implement security, access controls, logging, and compliance best practices.
Required Skills
  • 5+ years of experience in DevOps, ML Engineering, MLOps, or a related field.
  • Strong experience with MLOps concepts and ML lifecycle management.
  • Hands-on experience with Python and scripting.
  • Experience with AWS, Azure, or GCP.
  • Strong knowledge of Docker and Kubernetes.
  • Experience with CI/CD tools such as Jenkins, GitHub Actions, GitLab CI, or Azure DevOps.
  • Experience with MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML, or similar ML platforms.
  • Experience with Git, Terraform, and infrastructure automation.
  • Knowledge of model monitoring, observability, data validation, and model performance tracking.
  • Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures.
Preferred Skills
  • Experience with Apache Airflow, Databricks, Spark, or Kafka.
  • Knowledge of LLMOps/GenAI deployment and monitoring.
  • Experience with model serving frameworks such as KServe, Seldon, or NVIDIA Triton.
  • Familiarity with Prometheus, Grafana, ELK, or similar observability tools.
  • Understanding of ML security, governance, and responsible AI practices.
Education

Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.

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