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Computer Science Engineer Jobs in Milwaukee, WI (NOW HIRING)

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Computer Science Engineer information

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$47.7K

$119.6K

$135.4K

How much do computer science engineer jobs pay per year?

As of Sep 10, 2026, the average yearly pay for computer science engineer in Milwaukee, WI is $119,618.00, according to ZipRecruiter salary data. Most workers in this role earn between $109,800.00 and $129,400.00 per year, depending on experience, location, and employer.

What is a computer science engineer?

Computer Science Engineers are professionals who apply principles of computer science, engineering, and mathematics to design, develop, test, and maintain computer systems, software, and networks. They work on a wide range of technologies, from creating applications and operating systems to improving network security and optimizing hardware performance. Their role often involves problem-solving, programming, and collaborating with other engineers and IT specialists to deliver efficient technology solutions.

What do computer science engineers do?

Computer Science Engineers often collaborate with professionals from diverse backgrounds such as product managers, designers, and data analysts. Typical projects may include developing scalable software applications, optimizing algorithms for performance, or integrating new technologies into existing systems. These engineers are frequently involved in code reviews, planning meetings, and cross-functional brainstorming sessions to ensure solutions meet both technical and business requirements. This collaborative environment not only enhances technical skills but also fosters communication and project management abilities.

What are the key skills and qualifications needed to thrive as a computer science engineer?

To thrive as a Computer Science Engineer, you need a solid background in programming, algorithms, data structures, and computer systems, often supported by a degree in computer science or a related field. Familiarity with languages like Python, Java, or C++, as well as experience using development tools, version control systems (such as Git), and relevant certifications like AWS or Cisco, are highly valued. Strong problem-solving abilities, teamwork, and effective communication help set exceptional engineers apart. These skills are vital for designing, developing, and maintaining reliable software solutions in ever-evolving technical environments.

Do computer science engineers make good money?

Computer science engineers typically earn competitive salaries that vary based on experience, location, and industry. Entry-level positions often start with moderate pay, while experienced engineers working in specialized fields or in high-demand areas can earn significantly higher salaries, especially with skills in programming, software development, and systems design.

What are popular job titles related to Computer Science Engineer jobs in Milwaukee, WI?

For Computer Science Engineer jobs in Milwaukee, WI, the most frequently searched job titles are:

What job categories do people searching Computer Science Engineer jobs in Milwaukee, WI look for?

The top searched job categories for Computer Science Engineer jobs in Milwaukee, WI are:

What cities near Milwaukee, WI are hiring for Computer Science Engineer jobs?

Cities near Milwaukee, WI with the most Computer Science Engineer job openings:

Computer Science Engineer jobs near you
Infographic showing various Computer Science Engineer job openings in Milwaukee, WI as of August 2026, with employment types broken down into 1% As Needed, 82% Full Time, 13% Part Time, 3% Contract, and 1% Nights. Highlights an 79% Physical, 2% Hybrid, and 19% Remote job distribution, with an average salary of $119,722 per year, or $57.6 per hour.

ML Ops Engineer

Richfield, WI • On-site

Techvilla Solutions
IT Services • 51 - 200 employees
Learn more about Techvilla Solutionsexternal

Full-time

Posted 20 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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