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Mlops Machine Learning Engineer Jobs in Madison, WI

AgSource

Senior AI/ML Engineer

Watertown, WI · On-site +1

$99K - $136K/yr

... machine learning to modern generative AI, choosing the simplest tool that solves the problem ... Model Fine-Tuning & MLOps * Integrate, deploy, fine tune and monitor models in production using ...

Farm Systems

Senior AI/ML Engineer

Watertown, WI

$99K - $136K/yr

... machine learning to modern generative AI, choosing the simplest tool that solves the problem ... Model Fine-Tuning & MLOps * Integrate, deploy, fine tune and monitor models in production using ...

Abbott Laboratories

Sr. Engineer, AI Platform

Madison, WI · On-site

$99K - $198K/yr

Position Overview The Lead Engineer, Artificial Intelligence is a pivotal position responsible for ... This role demands expertise in Machine Learning, Natural Language Processing, and emerging ...

Springs Window Fashions

Senior AI Engineer

Middleton, WI · On-site

$107K - $147K/yr

Design, build, ship, and own enterprise AI and machine learning solutions in production. * Build ... 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, ship, and own enterprise AI and machine learning solutions in production. * Build ... 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, ship, and own enterprise AI and machine learning solutions in production. * Build ... Experience with MLOps, vector databases, orchestration frameworks, and AI observability platforms.

Abbott

Sr. Engineer, AI Platform

Madison, WI · On-site

$99K - $198K/yr

Position Overview The Lead Engineer, Artificial Intelligence is a pivotal position responsible for ... This role demands expertise in Machine Learning, Natural Language Processing, and emerging ...

Abbott

Sr. Engineer, AI Platform

Madison, WI · On-site

$99K - $198K/yr

Position Overview The Lead Engineer, Artificial Intelligence is a pivotal position responsible for ... This role demands expertise in Machine Learning, Natural Language Processing, and emerging ...

Abbott

Sr. Engineer, AI Platform

Madison, WI · On-site

$99K - $198K/yr

Position Overview The Lead Engineer, Artificial Intelligence is a pivotal position responsible for ... This role demands expertise in Machine Learning, Natural Language Processing, and emerging ...

Insperity

Artificial Intelligence Engineer III

Madison, WI · On-site

$58 - $77.75/hr

This role applies advanced software, data science, machine learning, and LLM engineering expertise to build AI powered applications, model driven solutions, and intelligently automated workflows. 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 Sep 9, 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.

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.

What are the key skills and qualifications needed to thrive as an MLOps machine learning engineer?

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.

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 machine learning engineers in demand?

MLOps machine learning engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects and competitive salaries.

Do MLOps Machine Learning Engineers need a degree?

MLOps Machine Learning Engineers typically do not require a formal degree but often have a background in computer science, data science, or related fields. Practical skills in machine learning, cloud platforms, and tools like Docker, Kubernetes, and CI/CD pipelines are highly valued. Certifications and hands-on experience can also enhance job prospects.

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 September 2026, with employment types broken down into 1% Internship, 1% As Needed, 72% Full Time, 25% Part Time, and 1% Contract. Highlights an 82% Physical, 2% Hybrid, and 16% Remote job distribution, with an average salary of $129,751 per year, or $62.4 per hour.

Senior AI/ML Engineer

Watertown, WI • On-site, Remote

AgSource
Crop Farming • 51 - 200 employees
Learn more about AgSourceexternal

$99K - $136K/yr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Key responsibilities

  • Lead the building of scalable real-time production-grade AI/ML applications that provide actionable intelligence on dairy farms.

  • Identify opportunities to apply AI for efficiency, growth, and customer value, and demonstrate how AI can address customer needs.

  • Establish AI governance frameworks, track AI adoption through KPIs, and communicate the impact of AI initiatives.

Job description


Turn decades of data into intelligence that helps feed the world.
VAS is the Operating System of the modern dairy with decades of longitudinal data for the most productive cows in the world. We hold a dominant US market position, and an expanding global reach.
We are seeking a Senior AI/ML Engineer to lead the building of scalable real-time production-grade applications that use AI/ML models to drive actionable intelligence on dairy farms. This is a strategic, hands-on position for an experienced technical leader who has a track record of shipping AI-enhanced customer applications and tooling used by engineering teams.
Our highly customizable on-farm systems give dairy owners unmatched flexibility in how they run their business. The right candidate sees that as a data challenge, where others will see it as an unsolvable mess.
RESPONSIBILITIES
AI Enablement
  • Understand customer challenges and how integrating AI capabilities can help lead to solutions that have AI as a differentiator.
  • Identify opportunities to apply AI for efficiency, growth, and customer value
  • Drive awareness of AI capabilities and demonstrate how it can address customer needs, improve efficiency, reduce costs, and drive growth
  • Drive transformation from AI-Ad Hoc to AI-Native engineering practices
  • Serve as an AI technical SME, conduct R&D to meet the needs of our AI strategy
  • Continuously assess emerging AI tools and make data-driven recommendations
  • Measure & Accelerate Adoption: Establish KPIs, track progress from the current to 100% adoption, implement interventions to accelerate uptake and communicate impact
  • Build Center of Excellence: Create forums for knowledge sharing, celebrate wins, and foster peer-to-peer learning
  • Cross-functional communication, explaining technical tradeoffs to product, dairy science, and engineering leadership in plain language.
  • Working with other enterprise stakeholders, establish AI governance frameworks and guardrails covering compliance, security, privacy, and ethical AI practices, and embed them into development workflows

Core AI Engineering Skills
  • Comfort across the full method spectrum, from classical statistics and operations research through machine learning to modern generative AI, choosing the simplest tool that solves the problem.
  • Data-wrangling skill with messy, distributed, legacy enterprise data sources, including inconsistent schemas and incomplete records.
  • Feature-engineering and data preprocessing for both structured farm data and unstructured sources.
  • Model selection and evaluation, knowing when linear regression, optimization, or a lookup table beats a neural network.
  • Production deployment experience, shipping models into real time applications rather than notebooks.
  • Cloud AI infrastructure fluency, specifically Databricks and AWS.
  • Experiment design and statistical rigor, being able to prove a model or method actually improves outcomes.
  • Translating ambiguous business or technical requirements into working systems.
  • Agentic and MCP experience

Evaluation, Testing & Observability
  • Build unit and behavioral tests for agents, tools, and workflows.
  • Develop tooling for trace analysis, agent state debugging, and hallucination tracking.
  • Compare and benchmark agent orchestration frameworks for trade-offs in speed, reliability, and usability.

Model Fine-Tuning & MLOps
  • Integrate, deploy, fine tune and monitor models in production using cloud providers.
  • Set up agent logging, observability dashboards, and recovery workflows.

Front-end & User Experience
  • Collaborate with front-end developers or build user-facing components using React, TypeScript.
  • Ensure seamless user and agent interaction via UI and API bridges.

EDUCATION & EXPERIENCE
Your background might include software engineering, data engineering, data science, machine learning engineering or AI engineering. What matters most is demonstrated technical depth and a track record of building and deploying AI/ML solutions in production.
  • Significant hands-on experience designing, building and deploying production AI/ML solutions.
  • Strong experience working with complex data, including distributed systems, inconsistent schemas and incomplete or legacy datasets.
  • Experience with feature engineering, model selection, experimentation and evaluation.
  • Strong understanding of descriptive, predictive, prescriptive and generative AI approaches.
  • Experience selecting and applying techniques across statistics, operations research, machine learning and deep learning.
  • Demonstrated experience taking models from experimentation through production deployment and monitoring.
  • Experience with deep learning frameworks and cloud-based AI services.
  • Experience with AWS and/or Databricks.
  • Experience or exposure to agentic architectures, MCP and AI orchestration frameworks.
  • Strong software engineering fundamentals and experience building scalable, production-quality systems.
  • Ability to translate ambiguous requirements into working solutions and clearly communicate technical decisions and tradeoffs.
  • Bachelor's degree in Software Engineering, Computer Science, Data Science, AI/ML or a related field preferred.

About Us
For the past 40 years we've woken up each day to support those that never stop feeding the world - and we have no plans to quit. We set the standard for farm management solutions and fix our eyes on raising the bar to meet the next generation of expectations.
Our software and information solutions help collect and connect a farm's data - from herd management to feed performance, tracking and more. These insights are a source of truth, empowering producers and their trusted advisors to make profit-driven and sustainable management decisions.
Whether near or far, large or small, VAS is at the heart of your dairy.
VAS has deep roots in the industry through its origin within the URUS family of companies. As a holding company with cooperative and private ownership, URUS is a family of businesses at the heart of the dairy and beef industry - Alta Genetics, GENEX, Genetics Australia, Leachman Cattle, Jetstream, PEAK, SCCL, Trans Ova Genetics and VAS. Each organization has its unique identity, products, and services. These companies work globally to provide cutting-edge dairy and beef genetics, customized reproductive services to maximize conceptions, dairy management information to take producers to the frontline of progressive dairy farming, and an array of products and services to help bovines reach their full genetic potential. URUS has 9 brands in 17 retail countries and employs nearly 2,800 people globally.

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