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Machine Learning Operations Jobs in Illinois (NOW HIRING)

TEKsystems is seeking a Machine Learning Engineer to support one of our major customers that sits ... Our expertise in strategy, design, execution and operations unlocks business value through a range ...

Machine Learning Researcher

Chicago, IL · On-site

$250K - $300K/yr

Design and deploy machine learning models to enhance trading performance across various asset ... business operations professionals are united by our uniquely collaborative, high-performance ...

Machine Learning Lead

Chicago, IL · On-site

$225K - $275K/yr

Partner with Engineering, Product, and Operations to embed fraud intelligence directly into payment ... in machine learning, applied data science, or production ML roles * Demonstrated experience ...

IMC Trading is seeking a Machine Learning Research Lead with proven experience applying ... business operations professionals are united by our uniquely collaborative, high-performance ...

We are deploying machine learning directly onto custom hardware - and we want you to help drive it ... business operations professionals are united by our uniquely collaborative, high-performance ...

We are deploying machine learning directly onto custom hardware - and we want you to help drive it ... business operations professionals are united by our uniquely collaborative, high-performance ...

... operational efficiency, and expanding diagnostic possibilities. About the Role We are seeking an ... The ideal candidate will have deep expertise in Machine Learning and building generalizable ...

Showing results 41-60

Machine Learning Operations information

What are machine learning operations?

Machine Learning Operations (MLOps) is a set of practices that combines machine learning, software engineering, and DevOps to deploy, monitor, and maintain machine learning models in production environments. It involves tasks such as model versioning, automation, testing, and ensuring scalability and reliability using tools like CI/CD pipelines and cloud platforms.

What is the difference between Machine Learning Operations vs Data Scientist?

AspectMachine Learning OperationsData Scientist
Primary FocusDeploying, maintaining, and scaling ML models in productionAnalyzing data to develop insights and build models
Required SkillsML deployment, cloud platforms, automation, scriptingStatistical analysis, data visualization, programming (Python/R)
Work EnvironmentOperations teams, cloud infrastructure, production systemsResearch environments, data analysis teams, R&D
Common CertificationsCloud certifications, MLOps tools certificationsData science certifications, statistical courses

Machine Learning Operations and Data Scientists often collaborate, but MLOps focuses on deploying and maintaining models in production, while Data Scientists focus on analyzing data and developing models. Both roles require technical skills, but their day-to-day tasks and environments differ.

Is machine learning operations a high paying job?

Machine Learning Operations (MLOps) roles typically offer high salaries due to the specialized skills required, such as expertise in cloud platforms, automation, and data engineering. Compensation varies based on experience, location, and company size, but generally ranks above average compared to other tech roles.
What cities in Illinois are hiring for Machine Learning Operations jobs? Cities in Illinois with the most Machine Learning Operations job openings:
Infographic showing various Machine Learning Operations job openings in Illinois as of August 2026, with employment types broken down into 87% Full Time, 10% Part Time, 1% Temporary, and 2% Contract. Highlights an 94% Physical, 2% Hybrid, and 4% Remote job distribution.

Machine Learning Engineering Manager

United Airlines, Inc.

Chicago, IL • On-site

$118K - $141K/yr

Full-time

Posted 5 days ago


United Airlines rating

7.9

Company rating: 7.9 out of 10

Based on 341 frontline employees who took The Breakroom Quiz

7th of 26 rated airlines


Job description

Description
Job overview and responsibilities
Develops and programs integrated software algorithms to structure, analyze and leverage data in systems applications. Develops and communicates statistical modeling techniques to develop and evaluate algorithms to improve product/system performance, quality, data management and accuracy. Completes programming and implements efficiencies, performs testing and debugging. Completes documentation and procedures for installation and maintenance. Applies deep learning technologies to give computers the capability to visualize, learn and respond to complex situations. Can work with large scale computing frameworks, data analysis systems and modeling environments.
  • Design and implement key components of the Machine Learning Platform infrastructure and establish processes and best practices
  • Work cross-functionally with data scientists, data engineers, and IT teams to design, develop, deploy, and integrate high-performance, production-grade machine learning solutions and data intensive workflows
  • Partner with data scientists and data engineers to create and refine features from underlying data and build reproducible feature pipelines to train models and serve features in production
  • Partner with data platform and operations teams to solve complex data ingestion, pipeline and governance problems for machine learning solutions
  • Take ownership of production systems with a focus on delivery, continuous integration, and automation of machine learning workloads
  • Provide technical mentorship, guidance, and quality-focused code review to data scientists and ML engineers

Qualifications
What's needed to succeed (Minimum Qualifications):
  • Bachelor's degree in computer science, engineering, or a related technical discipline
  • 3+ years of experience in managing technical teams and projects
  • 3+ years of experience in full software lifecycle development using Python
  • 3+ years of experience leading an ML Ops team familiar with large cloud environments, Big Data technologies
  • 3+ years in software development in Python, Java, PySpark
  • 3+ Years of Experience with Machine Learning and Machine Learning workflows
  • 3+ years of experience designing and developing using technologies as Docker, Kubernetes
  • Strong software engineering experience with Python and at least one additional language such as Java, Go, Rust, or C/C++
  • Understanding of machine learning principles and techniques
  • Experience with data science tools and frameworks (e.g. PyTorch, Tensorflow, Keras, Pandas, Numpy, Spark)
  • Experience designing and developing scalable cloud native solutions using technologies such as Docker and Kubernetes and serverless services such as AWS Lambda, EKS, ECS, Fargate
  • Experience building infrastructure-as-code templates (e.g. AWS CloudFormation) and cloud-native CI/CD pipelines using tools such as AWS CodePipeline
  • Experience building ETL pipelines and working with big data technologies (e.g. Hadoop, Spark, and serverless technologies such as EMR, Redshift, S3, AWS Glue, and Kinesis)
  • Knowledge of distributed systems as it pertains to compute and data storage
  • Strong desire to experiment with and learn new technologies and stay aligned with the latest community developments in ML Ops/Engineering and cloud native
  • Excellent oral and written communication skills. Ability to prepare high-quality presentation materials and explain complex concepts and technical materials to less-technical audiences
  • Must be legally authorized to work in the United States for any employer without sponsorship
  • Successful completion of interview required to meet job qualification
  • Reliable, punctual attendance is an essential function of the position

What will help you propel from the pack (Preferred Qualifications):
  • AWS Certified Solution Architect (Associate or Professional)
  • Experience working as a Machine Learning Engineer or Data Scientist building and productional machine learning solutions
  • Experience building real-time event-driven stream processing solutions with technologies such as Kafka, Flink, and Spark
  • Experience with GPU acceleration (e.g. CUDA and CuDNN)
  • Experience with Kubernetes

What United Airlines employees say

Pay

Benefits

Hours and flexibility

Workplace

Get the full story on Breakroom


United Airlines logo

About United Airlines

Sourced by ZipRecruiter

United Airlines is embarking on an exciting journey to become the best airline in aviation history. Our purpose, "Connecting People, Uniting the World," extends beyond transportation, emphasizing our commitment to uplift and create opportunities in the places we serve. With a global presence and diverse workforce, we value inclusivity and are dedicated to hiring tens of thousands of individuals across various roles. Our comprehensive benefits package, including perks like space available travel, parental leave, and 401k, aims to support your well-being and growth.

Industry

Aviation

Company size

10,000+ Employees

Headquarters location

Chicago, IL, US

Year founded

1926

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