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Senior Machine Learning Ops Engineer Jobs in Schaumburg, IL

Oversee teams of data scientists, modelers, and ML engineers to deliver innovative and scalable ... Knowledge of ML Ops, model governance, and lifecycle management best practices. * Demonstrated ...

Senior Dev Ops Engineer

Northbrook, IL · On-site

$100K - $125K/yr

Description Ecentria is looking to add a Senior Dev Ops Engineer to our team! This hybrid role requires employees to work onsite in our Northbrook, IL office once per week. Responsibilities Include:

New

Senior AI Machine Learning Engineer

Chicago, IL · On-site

$126K - $166K/yr

Sr Data Engineer - GE07BE We're determined to make a difference and are proud to be an insurance ... The Hartfordis seeking aSenior AI Machine Learning Engineerwithin Employee Benefits Applied AI and ...

Machine Learning Engineer

Chicago, IL · On-site

$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 ...

Showing results 41-60

Senior Machine Learning Ops Engineer information

See Schaumburg, IL salary details

$58.4K

$124.3K

$180.2K

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

As of Sep 3, 2026, the average yearly pay for senior machine learning ops engineer in Schaumburg, IL is $124,274.00, according to ZipRecruiter salary data. Most workers in this role earn between $102,600.00 and $140,900.00 per year, depending on experience, location, and employer.

What is a senior machine learning ops engineer?

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 are the key skills and qualifications needed to thrive as a senior machine learning ops engineer?

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 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 popular job titles related to Senior Machine Learning Ops Engineer jobs in Schaumburg, IL?

For Senior Machine Learning Ops Engineer jobs in Schaumburg, IL, the most frequently searched job titles are:

What cities near Schaumburg, IL are hiring for Senior Machine Learning Ops Engineer jobs?

Cities near Schaumburg, IL with the most Senior Machine Learning Ops Engineer job openings:

AVP, Machine Learning & Modeling

Vizient, Inc.

Chicago, IL

$156K - $290K/yr

Full-time

Posted 16 days ago


Job description

When you're the best, we're the best. We instill an environment where employees feel engaged, satisfied and able to contribute their unique skills and talents while living and working as their authentic selves. We provide extensive opportunities for personal and professional development, building both employee competence and organizational capability to fuel exceptional performance through an inclusive environment both now and in the future.

In this role, you will lead the design, development, and implementation of advanced analytics, statistical modeling, and artificial intelligence solutions to drive data-informed decision-making across the enterprise. Oversee teams of data scientists, modelers, and ML engineers to deliver innovative and scalable solutions that support strategic business objectives, operational excellence, and client value creation. Serve as a key thought leader, guiding the organization's data science strategy, ensuring model governance and compliance, and advancing the use of cutting-edge machine learning methodologies.

Role Responsibilities: Strategic Leadership and Vision Provide strategic direction for the organization's machine learning and modeling initiatives. Develop and execute a roadmap for data-driven innovation aligned with corporate goals and emerging technologies in AI, predictive analytics, and optimization. Model Development and Deployment Oversee the design, development, and validation of statistical, econometric, and machine learning models that support forecasting, risk assessment, operational efficiency, and client performance improvement.

Ensure model accuracy, interpretability, and ethical use of data. Model Governance and Compliance Establish and maintain robust model governance frameworks, including documentation, version control, validation, and ongoing performance monitoring. Partner with risk management and compliance teams to ensure adherence to regulatory and ethical standards.

Team Development and Leadership Lead and mentor a multidisciplinary team of data scientists, ML engineers, and quantitative analysts. Foster a culture of innovation, continuous learning, and collaboration while ensuring alignment with business priorities and project timelines. Cross-Functional Collaboration Collaborate with business leaders, IT, and analytics stakeholders to identify opportunities for data science applications that drive measurable business outcomes.

Translate complex analytical results into actionable insights for executive decision-making. Research and Innovation Stay at the forefront of emerging AI and machine learning technologies. Evaluate and integrate new tools, frameworks, and methodologies to enhance model performance and scalability.

Promote experimentation and best practices in applied data science. Requirements: Bachelors or Masters or Doctorate degree in Data Science, Statistics, Computer Science, Applied Mathematics, Engineering, Economics, or a related quantitative field preferred Expertise in machine learning, deep learning, and statistical modeling techniques (e.g., regression, time series, NLP, ensemble methods, neural networks). Strong proficiency in Python, SQL, and with cloud-based AI/ML platforms (AWS SageMaker, Azure ML, Databricks, etc.) Knowledge of ML Ops, model governance, and lifecycle management best practices

Demonstrated ability to translate business problems into analytical frameworks and deliver measurable outcomes. Excellent leadership, communication, and stakeholder management skills, with the ability to present complex findings to non-technical audiences. Proven experience building and leading high-performing data science teams in a large, matrixed organization.

Familiarity with healthcare, financial, or operational analytics. Estimated Hiring Range: At Vizient, we consider skills, experience, and organizational needs in our compensation approach. Geographic factors may adjust the range estimate and hires typically fall below the top range.

Compensation decisions are tailored to individual circumstances. The current salary range for this role is $156,500.00 to $290,100.00. This position is also incentive eligible

Vizient has a comprehensive benefits plan. Please view our benefits here: http://www.vizientinc.com/about-us/careers Equal Opportunity Employer: Females/Minorities/Veterans/Individuals with Disabilities The Company is committed to equal employment opportunity to all employees and applicants without regard to race, religion, color, gender identity, ethnicity, age, national origin, sexual orientation, disability status, veteran status or any other category protected by applicable law.