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Mlops Machine Learning Engineer Jobs in Itasca, IL

Accord Technologies Inc.

MLOPS Engineer

Chicago, IL · On-site

MLOPS Engineer Location: Chicago, IL Duration: 12+ months Position type: W2 contract Required Skills f or the MLOps Engineer: - Bachelor's plus 9+ years of experience, Master ...

Crate and Barrel

AI/ ML Engineer

Northbrook, IL · On-site

$80 - $120/hr

We are looking for an AI / Machine Learning Engineer with 2-5 years of experience to play a ... This role is ideal for an engineer eager to solidify their production skills, learn MLOps best ...

IMC

Hardware Machine Learning Engineer

Chicago, IL

$127K - $167K/yr

... engineers to implement, verify, and deploy ML inference solutions from proof-of-concept through production * Track and evaluate emerging research in neural architecture search, machine learning ...

Crowe LLP

Machine Learning Engineer

Chicago, IL

$62K - $100K/yr

As an AI Engineering team member, you will be instrumental in advancing new features and/or solutions from the Proof of Concept stage to full production readiness. Your role involves refining and ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

... engineers to implement, verify, and deploy ML inference solutions from proof-of-concept through production * Track and evaluate emerging research in neural architecture search, machine learning ...

IMC

Hardware Machine Learning Engineer

Chicago, IL · On-site

$200K - $225K/yr

... engineers to implement, verify, and deploy ML inference solutions from proof-of-concept through production * Track and evaluate emerging research in neural architecture search, machine learning ...

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All JobsMlops Machine Learning Engineer JobsMlops Machine Learning Engineer Jobs in Itasca, IL

Mlops Machine Learning Engineer information

See Itasca, IL salary details

$30.7K

$125.4K

$188.4K

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

As of Sep 2, 2026, the average yearly pay for mlops machine learning engineer in Itasca, IL is $125,377.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,800.00 and $150,900.00 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 cities near Itasca, IL are hiring for Mlops Machine Learning Engineer jobs?

Cities near Itasca, IL 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 Itasca, IL as of August 2026, with employment types broken down into 1% As Needed, 65% Full Time, 33% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $125,377 per year, or $60.3 per hour.

MLOPS Engineer

Accord Technologies Inc.

Chicago, IL • On-site

Contractor

Re-posted 2 days ago

Job description

Title: MLOPS Engineer
Location: Chicago, IL
Duration: 12+ months
Position type: W2 contract
 
 
Required Skills for the MLOps Engineer:
- Bachelor's plus 9+ years of experience, Master's plus 6+ years of experience
- Experience working with an object-oriented programming language (Python, Golang, Java, C/C++ etc.)
- Experience with MLOps frameworks like MLflow, Kubeflow, etc
- Proficiency in programming (Python, R, SQL)
- Ability to design and implement cloud solutions and build MLOps pipelines on cloud solutions (e.g., AWS)
- Strong understanding of DevOps principles and practices, CI/CD, etc. and tools (Git, GitHub, jFrog Artifactory, Azure DevOps, etc.)
- Experience with containerization technologies like Docker and Kubernetes
- Strong communication and collaboration skills
- Ability to help work with a team to create User Stories and Tasks out of higher-level requirements
 
Preferred Skills:
- Ability to create model inference systems with advanced deployment methods that integrate with other MLOps components like MLFlow
- Knowledge of inference systems like Seldon, Kubeflow, etc
- Knowledge of deploying applications and systems in Langfuse or Kubernetes using Helm and Helmfile
- Knowledge of infrastructure orchestration using CloudFormation or Terraform
- Exposure to observability tools (such as Evidently AI)
 
MLOps Engineer Overview:
The MLOps Platform Team works within the Enterprise Data and Analytics Organization driving the ability to work with Internal Teams to be able to support the full life-cycle of AI and machine learning development through to beyond production. Helping build a platform that enables data driven decisions across the enterprise, helping teams build high-value data and AI/ML products, and enable the operationalization and reliability of all models. We are searching for a driven and highly skilled MLOps Engineer to join our MLOps Platform team at ServiceNow. The role will build the MLOps Platform, build self-service ML Development tooling, and building platform adoption.
 
Responsibilities:
- Define scalable and secure architectures, frameworks and pipelines for building, deploying and diagnosing production ML applications
- Enable users & teams on the ML platform; troubleshoot and debug user issues; maintain user-friendly documentation and training
- Collaborate with internal stakeholders to build a comprehensive MLOps Platform
- Design and implement cloud solutions and build MLOps pipelines on cloud solutions (e.g., AWS)
- Develop standards and examples to accelerate the productivity of data science teams
- Run code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality, including data & concept drift
- Create way to automate the testing, validation, and deployment of data science models
- Provide best practices and execute POC for automated and efficient MLOps at scale
 
Education Requirements:
- Bachelor's degree or Master's degree