Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Lannon, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Lannon, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Wales, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Wales, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Menomonee Falls, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Menomonee Falls, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Waukesha, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Waukesha, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Mequon, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Mequon, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Elm Grove, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Elm Grove, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Whitefish Bay, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Whitefish Bay, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
North Lake, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
North Lake, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Greendale, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Greendale, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Hubertus, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Hubertus, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Merton, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Merton, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Sussex, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Sussex, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Pewaukee, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Pewaukee, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Big Bend, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Big Bend, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Wauwatosa, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Wauwatosa, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Colgate, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Colgate, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Milwaukee, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Milwaukee, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Bayside, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Bayside, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Brookfield, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
ML Ops Engineer
Brookfield, WI · On-site
Knowledge of model monitoring, observability, data validation, and model performance tracking. * Strong understanding of REST APIs, microservices, Linux, and cloud-native architectures. Preferred ...
Observability information
See Wisconsin salary details
$16.50 - $22.92
0% of jobs
$22.92 - $29.34
0% of jobs
$29.34 - $35.76
2% of jobs
$35.76 - $42.17
5% of jobs
$42.17 - $48.59
10% of jobs
$51.60 is the 25th percentile. Wages below this are outliers.
$48.59 - $55.01
17% of jobs
The median wage is $60.08 / hr.
$55.01 - $61.43
20% of jobs
$61.43 - $67.85
18% of jobs
$69 is the 75th percentile. Wages above this are outliers.
$67.85 - $74.27
15% of jobs
$74.27 - $80.69
9% of jobs
$80.69 - $87.11
4% of jobs
$16
$61
$87
How much do observability jobs pay per hour?
As of Sep 13, 2026, the average hourly pay for observability in Wisconsin is $61.10, according to ZipRecruiter salary data. Most workers in this role earn between $51.20 and $70.14 per hour, depending on experience, location, and employer.
What is an observability?
An Observability job focuses on ensuring the performance, reliability, and health of software systems by collecting, analyzing, and visualizing telemetry data such as logs, metrics, and traces. Professionals in this field work with monitoring tools, distributed tracing, and alerting systems to detect and troubleshoot issues proactively. They collaborate with engineering and operations teams to improve system visibility, reduce downtime, and enhance overall system performance.
What does an observability do?
In an Observability role, your daily tasks often include designing and maintaining monitoring dashboards, configuring alerts, analyzing system logs, and working closely with development and operations teams to troubleshoot issues. You'll proactively identify areas of improvement to increase system reliability, document monitoring strategies, and support incident response efforts. Collaboration is key, as you may participate in post-incident reviews and help drive architectural improvements based on the data you collect. The role is dynamic and requires a proactive approach to ensure systems stay healthy and downtime is minimized.
What are the key skills and qualifications needed to thrive in an observability role?
To thrive in an Observability role, you need a strong background in monitoring, alerting, logging, and analyzing system performance, often supported by a degree in computer science or related field. Familiarity with tools such as Prometheus, Grafana, Datadog, Splunk, and experience with cloud platforms and scripting languages is crucial. Excellent problem-solving, communication, and collaboration skills help you work effectively with cross-functional engineering and operations teams. These capabilities are essential to ensure system reliability, quickly detect issues, and maintain seamless digital experiences.
Is observability a good career?
Observability is a growing field within IT and software engineering that involves monitoring, logging, and analyzing system performance using tools like Prometheus, Grafana, and Elasticsearch. It offers opportunities for specialization, high demand for skills, and roles in DevOps and site reliability engineering, making it a viable career choice for those interested in system reliability and automation.
What are the most commonly searched types of Observability jobs in Wisconsin?
The most popular types of Observability jobs in Wisconsin are:
What are popular job titles related to Observability jobs in Wisconsin?
For Observability jobs in Wisconsin, the most frequently searched job titles are:
What job categories do people searching Observability jobs in Wisconsin look for?
The top searched job categories for Observability jobs in Wisconsin are:
ML Ops Engineer
Butler, WI
Techvilla Solutions
 • IT Services • 51 - 200 employees51 - 200 employees
Full-time
Posted 23 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.
- 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.
- 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.
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related technical field, or equivalent practical experience.
About Techvilla Solutions
Sourced by ZipRecruiter
Industry
It services
Company size
51 - 200 Employees
Headquarters location
CA, US
Year founded
2006