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Senior Machine Learning Ops Engineer Jobs in Chicago, IL

Ontrac Solutions is seeking Machine Learning Engineers to support an urgent staff augmentation ... senior architect. Key Responsibilities * Support the design, deployment, monitoring, and ...

Ontrac Solutions is seeking Machine Learning Engineers to support an urgent staff augmentation ... senior architect. Key Responsibilities * Support the design, deployment, monitoring, and ...

Collaborate with senior engineers and data scientists on model deployment. * Conduct experiments and run machine learning tests. * Stay updated with the latest advancements in machine learning.

Collaborate with senior engineers and data scientists on model deployment. * Conduct experiments and run machine learning tests. * Stay updated with the latest advancements in machine learning.

Collaborate with senior engineers and data scientists on model deployment. * Conduct experiments and run machine learning tests. * Stay updated with the latest advancements in machine learning.

Senior Machine Learning Engineer (LLMs)

Chicago, IL · On-site

$126.20K - $166.40K/yr

... engineers * An environment that values deep work, clear thinking, and real impact * Regular team events and off-sites * Equipment and learning budget to help you do your best work and keep up with ...

Senior Machine Learning Engineer (LLMs)

Chicago, IL · On-site

$126.20K - $166.40K/yr

... engineers * An environment that values deep work, clear thinking, and real impact * Regular team events and off‑sites * Equipment and learning budget to help you do your best work and keep up with ...

Senior Machine Learning Engineer (LLMs)

Chicago, IL

$126.20K - $166.40K/yr

... engineers * An environment that values deep work, clear thinking, and real impact * Regular team events and offsites * Equipment and learning budget to help you do your best work and keep up with the ...

Senior Machine Learning Engineer (LLMs)

Chicago, IL · On-site

$126.30K - $166.50K/yr

... engineers * An environment that values deep work, clear thinking, and real impact * Regular team events and off‑sites * Equipment and learning budget to help you do your best work and keep up with ...

Senior ML Engineer

Chicago, IL · Remote

$180K - $240K/yr

Career Renew is recruiting for one of its clients a Senior Machine Learning Engineer - this is a fully remote role for US/Canada based candidates. Salary range: 180-240K USD plus benefits plus equity.

Machine Learning Engineer

Chicago, IL · On-site

$175K - $250K/yr

Machine Learning Engineer Chicago, United States; Hong Kong, Hong Kong; Sydney, Australia As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and ...

Machine Learning Engineer

Chicago, IL · On-site

$175K - $250K/yr

As a Machine Learning Engineer, you will play a pivotal role in building systems that drive the training and deployment of large-scale ML models across our global operations. You'll collaborate with ...

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) Overview Darwill is a nationally recognized print and marketing communications firm based in the west suburbs of Chicago. As a premier provider of ...

MACHINE LEARNING ENGINEER (MLOPS / DATA ENGINEERING) Overview Darwill is a nationally recognized print and marketing communications firm based in the west suburbs of Chicago. As a premier provider of ...

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Senior Machine Learning Ops Engineer information

See Chicago, IL salary details

$61.3K

$130.4K

$189K

How much do senior machine learning ops engineer jobs pay per year?

As of May 28, 2026, the average yearly pay for senior machine learning ops engineer in Chicago, IL is $130,372.00, according to ZipRecruiter salary data. Most workers in this role earn between $107,600.00 and $147,800.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Senior Machine Learning Ops Engineer, and why are they important?

To thrive as a Senior Machine Learning Ops Engineer, you need expertise in machine learning, software engineering, cloud platforms, and experience with CI/CD pipelines, often supported by a computer science degree or equivalent experience. Proficiency with tools like Docker, Kubernetes, TensorFlow, PyTorch, and cloud services such as AWS, GCP, or Azure is typically required, along with familiarity with MLOps frameworks. Strong problem-solving, collaboration, and communication skills help you work effectively with cross-functional teams and manage complex ML model deployments. These skills are essential to ensure reliable, scalable, and efficient deployment of machine learning models in production environments.

What are some common challenges faced by Senior Machine Learning Ops Engineers when deploying models to production?

Senior Machine Learning Ops Engineers often encounter challenges such as ensuring model reproducibility, managing model versioning, and automating deployment pipelines for scalability. Another key challenge is monitoring model performance and data drift in production, which requires robust logging and alerting systems. Collaborating closely with data scientists, software engineers, and IT teams is essential to address these challenges and maintain a stable, efficient ML infrastructure.

What are Senior Machine Learning Ops Engineers?

Senior Machine Learning Ops (MLOps) Engineers are experienced professionals who design, build, and maintain the infrastructure and tools needed to deploy, monitor, and scale machine learning models in production environments. They work at the intersection of data science, software engineering, and DevOps to ensure ML models are robust, reliable, and secure. Their responsibilities often include automating model training pipelines, managing cloud resources, implementing CI/CD for ML, and ensuring model reproducibility. Senior MLOps Engineers also mentor junior staff and help define best practices for the organization’s ML workflow.

What is the difference between Senior Machine Learning Ops Engineer vs Data Engineer?

AspectSenior Machine Learning Ops EngineerData Engineer
CredentialsExperience with ML frameworks, cloud platforms, scripting, and DevOps toolsStrong SQL, ETL, database, and programming skills, often with cloud experience
Work EnvironmentFocus on deploying, monitoring, and maintaining ML models in productionDesigning and building data pipelines and infrastructure for data processing
Industry UsageCommon in AI/ML-focused companies, tech firms, and data-driven organizationsWidespread across industries for data management and analytics

While both roles involve working with data and cloud platforms, the Senior Machine Learning Ops Engineer specializes in deploying and maintaining machine learning models, whereas the Data Engineer focuses on building data pipelines and infrastructure. Understanding these distinctions helps in choosing the right career path or job search focus.

What are the most commonly searched types of Machine Learning Ops Engineer jobs in Chicago, IL? The most popular types of Machine Learning Ops Engineer jobs in Chicago, IL are:
What cities near Chicago, IL are hiring for Senior Machine Learning Ops Engineer jobs? Cities near Chicago, IL with the most Senior Machine Learning Ops Engineer job openings:

Machine Learning Engineer

Ontrac Solutions LLC

Chicago, IL • On-site

$70 - $90/hr

Contractor

Posted 29 days ago


Job description

Ontrac Solutions is seeking Machine Learning Engineers to support an urgent staff augmentation engagement for one of our clients.
This role is ideal for junior-to-mid-level engineers with strong Google Cloud Platform experience and a focus on building, maintaining, and supporting production-grade machine learning systems.
The selected engineers will work under the direct guidance of a Staff ML Architect and will focus heavily on daily MLOps execution, pipeline maintenance, model reliability, and production support for a high-traffic digital platform.
Required Credentials
  • 2+ years of experience in machine learning engineering, data engineering, software engineering, or a related technical role.
  • Hands-on experience supporting production or near-production ML systems.
  • Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or equivalent practical experience.
Required Qualifications
  • Solid hands-on experience with the GCP ecosystem, particularly Vertex AI components such as Workbench, Pipelines, and Model Registry.
  • Proficiency with modern ML frameworks, including PyTorch or similar technologies.
  • Experience with containerization tools, especially Docker, for automated builds and deployments.
  • Practical experience managing data processing workflows using Apache Spark and Airflow.
  • Understanding of MLOps best practices, including model deployment, monitoring, training workflows, inference support, and pipeline reliability.
  • Familiarity with real-time model serving and infrastructure tools such as Triton Inference Server and Terraform is highly preferred.
  • Strong problem-solving skills with the ability to troubleshoot, maintain, and optimize ML pipelines in a production environment.
  • Collaborative mindset with the ability to execute technical tasks reliably under the guidance of a senior architect.
Key Responsibilities
  • Support the design, deployment, monitoring, and maintenance of machine learning models in a high-traffic production environment.
  • Maintain, troubleshoot, and optimize end-to-end ML pipelines from raw data ingestion through offline and online model evaluation.
  • Execute daily MLOps tasks, including model training, inference support, pipeline monitoring, and deployment maintenance.
  • Work with tools such as GCP, Vertex AI, Spark, Airflow, Docker, PyTorch, and related MLOps technologies.
  • Build and manage automated containerized deployments to support continuous model operations.
  • Partner closely with the Staff ML Architect and other ML Engineers to ensure models are reliable, scalable, and production-ready.
  • Help identify and resolve performance, reliability, and scalability issues across ML workflows and infrastructure.
Preferred Qualifications
  • Prior experience supporting high-traffic digital platforms or consumer-facing products.
  • Experience with Triton Inference Server, Terraform, or similar infrastructure and real-time serving tools.
  • Experience working in staff augmentation, consulting, or fast-moving client-facing environments.
  • Strong interest in building reliable, production-grade ML systems rather than only experimental or research-focused models.
About Ontrac Solutions
Ontrac Solutions is a strategic consulting and technology solutions firm helping companies Innovate. Create. Elevate. through digital product consulting, cloud solutions, AI-based data solutions, and staff augmentation.
We partner with clients to bring the right technical expertise, execution support, and strategic guidance to complex business and technology initiatives.