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Mlops Machine Learning Engineer Jobs in Madison, WI

Springs Window Fashions

Senior AI Engineer

Middleton, WI

$107K - $147K/yr

Design, build, deploy, and maintain enterprise AI and machine learning solutions. * Develop and ... Experience with MLOps, vector databases, orchestration frameworks, and AI observability platforms.

Springs Window Fashions

Senior AI Engineer

Middleton, WI · On-site

$107K - $147K/yr

Design, build, deploy, and maintain enterprise AI and machine learning solutions. * Develop and ... Experience with MLOps, vector databases, orchestration frameworks, and AI observability platforms.

Cliftonlarsonallen

Senior AI Engineer

Madison, WI

$105K - $144K/yr

This position blends applied machine learning, software engineering, cloud architecture, and end-to ... Define and implement MLOps and LLMOps standards including versioning, deployment, monitoring, and ...

Exactsciences

AI Intern - Co-op Fall 2026

Madison, WI · On-site

$35K - $56K/yr

Essential Duties * Assist in the development, training, and evaluation of AI and machine learning ... Familiarity with MLOps concepts, model evaluation metrics, and responsible AI principles #LI-KL1 ...

SynergisticIT

Junior Software Engineer

Madison, WI · On-site

For data science/machine learning positions required skills bachelors degree or masters degree in ... computer engineering, electrical engineering, information systems, IT project work on the ...

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All JobsMlops Machine Learning Engineer JobsMlops Machine Learning Engineer Jobs in Madison, WI

Mlops Machine Learning Engineer information

See Madison, WI salary details

$31.7K

$129.8K

$195K

How much do mlops machine learning engineer jobs pay per year?

As of Jun 16, 2026, the average yearly pay for mlops machine learning engineer in Madison, WI is $129,751.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,300.00 and $156,200.00 per year, depending on experience, location, and employer.

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 Madison, WI? For Mlops Machine Learning Engineer jobs in Madison, WI, the most frequently searched job titles are:
What job categories do people searching Mlops Machine Learning Engineer jobs in Madison, WI look for? The top searched job categories for Mlops Machine Learning Engineer jobs in Madison, WI are:
What cities near Madison, WI are hiring for Mlops Machine Learning Engineer jobs? Cities near Madison, WI with the most Mlops Machine Learning Engineer job openings:
Mlops Machine Learning Engineer jobs near you
Infographic showing various Mlops Machine Learning Engineer job openings in Madison, WI as of June 2026, with employment types broken down into 63% Full Time, 27% Part Time, 5% Temporary, and 5% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $129,751 per year, or $62.4 per hour.
Senior AI Engineer

Senior AI Engineer

Springs Window Fashions

Middleton, WI

$107K - $147K/yr

Full-time

Posted 12 days ago

Springs Window Fashions rating

4.8

Company rating: 4.8 out of 10

Based on 5 frontline employees who took The Breakroom Quiz

Job description

The Best Experience Company 

Our tagline is “The Best Experience Company.” More than just a set of words, it represents the essence of who we are at Springs Window Fashions. As North America’s premier window covering company, we’re committed to creating the Best Experience for our associates, consumers and end users, business partners, and communities. We want you to join our team of passionate self-starters who believe the world is full of Best Experience opportunities. So, if you’re excited about the thought of a Best Experience career with a team focused on creating Best Experiences for all, we want to hear from you! 

Position Summary

The Sr AI Engineer serves as a technical leader responsible for enterprise-scale AI architecture, solution governance, and advanced AI engineering practices. This role drives strategic AI adoption and mentors engineering teams across the organization. This role will partner closely with Information Technology, business stakeholders, operations, customer service, product development, and analytics teams to deliver scalable AI capabilities that improve efficiency, enhance customer experiences, and enable data-driven decision making.

The ideal candidate combines strong software engineering fundamentals with practical expertise in machine learning, generative AI, data engineering, automation, and cloud technologies. This individual must be comfortable operating in a fast-paced, transformation-oriented environment and capable of translating business problems into production-ready AI solutions.

Key Responsibilities

  • Design, build, deploy, and maintain enterprise AI and machine learning solutions.
  • Develop and operationalize generative AI applications leveraging large language models (LLMs), retrieval-augmented generation (RAG), copilots, and intelligent automation.
  • Partner with business leaders to identify high-value AI use cases aligned to strategic priorities.
  • Build scalable AI pipelines, APIs, and integrations with enterprise platforms and business applications.
  • Collaborate with data engineering teams to ensure high-quality, governed, and accessible data for AI initiatives.
  • Develop AI-enabled analytics and predictive models supporting manufacturing, supply chain, customer service, sales, and operations.
  • Implement AI governance, model monitoring, security, and responsible AI practices.
  • Optimize model performance, scalability, reliability, and operational efficiency.
  • Evaluate emerging AI technologies and recommend enterprise adoption strategies.
  • Support AI experimentation, rapid prototyping, and innovation initiatives across the organization.
  • Create technical documentation, operational procedures, and knowledge transfer materials.
  • Mentor technical teams and promote AI engineering best practices.

Required

  • Bachelor’s degree in Computer Science, Information Technology, Data Science, Engineering, or a related field.
  • Advanced degree in Artificial Intelligence, Machine Learning, or Data Science preferred.
  • 8–10+ years overall technology experience
  • 5+ years focused in AI/ML engineering
  • Proven experience deploying enterprise-scale AI systems
  • Experience leading technical teams or major initiatives
  • Strong experience with AI architecture and distributed systems
  • Experience operationalizing generative AI at scale
  • Executive communication capability

Preferred

  • Experience with Microsoft Copilot, Azure OpenAI, or enterprise generative AI platforms.
  • Manufacturing, supply chain, consumer products, or retail industry experience.
  • Experience with MLOps, vector databases, orchestration frameworks, and AI observability platforms.
  • Familiarity with data visualization and analytics platforms such as Power BI or Tableau.
  • Experience leading enterprise AI transformation initiatives.
  • AI governance frameworks
  • FinOps for AI workloads
  • Multi-cloud AI strategy
  • Experience building internal AI platforms or copilots

How We Work to Deliver a Best Experience: Our Culture

  • Our Core Value: We do the right thing, always
  • Our Seven Cultural Behaviors
    • Empowerment - We trust our people.
    • Ownership - We take 100% responsibility for our roles actions, and results.
    • Leadership - We all lead by example and talk direct with respect (DWR).
    • One Team - We are One Springs Team.
    • Customer First - We consider our customers' needs before every decision.
    • Continuous Innovation - We are constantly learning, innovating, and improving.
    • Speed - We define priorities and operate with a sense of urgency and agility.

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