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Mlops Jobs in Ohio (NOW HIRING)

Huntington

MLOps Automation Senior Lead Engineer

Akron, OH · On-site +1

$99K - $130K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and deploying MLOps Automation for some of Huntington's most valuable and most challenging data-driven projects.

Huntington

MLOps Automation Senior Lead Engineer

Cleveland, OH · On-site +1

$100K - $132K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and deploying MLOps Automation for some of Huntington's most valuable and most challenging data-driven projects.

Huntington

MLOps Automation Senior Lead Engineer

Columbus, OH · On-site +1

$100K - $131K/yr

The MLOps Automation Engineering Senior Lead will lead a team responsible for building and deploying MLOps Automation for some of Huntington's most valuable and most challenging data-driven projects.

Flaschenpost

$79.89 - $125.54/hr

## Machine Learning Engineer (m/w/d) - MLOps & Software EngineeringBewerbenremote type: ITlocations: Office Münster: Office Köln: Remote: Office Berlintime type: Vollzeitposted on: Vor 2 Tagen ...

Huntington

MLOps Automation Engineer Lead

Columbus, OH

$99K - $130K/yr

Our Enterprise Data and Analytics department is growing, and we're looking for an outstanding MLOps Automation Engineer Lead to join our team. At Huntington, this is an opportunity to work cross ...

Huntington

MLOps Automation Engineer Lead

Columbus, OH · On-site

$140 - $190/hr

Description Summary Our Enterprise Data and Analytics department is growing, and we're looking for an outstanding MLOps Automation Engineer Lead to join our team. At Huntington, this is an ...

Huntington

MLOps Automation Engineer Lead

Columbus, OH

$70K - $140K/yr

Our Enterprise Data and Analytics department is growing, and we're looking for an outstanding MLOps Automation Engineer Lead to join our team. At Huntington, this is an opportunity to work cross ...

Flexjet

Director of AI Engineering

Cleveland, OH · On-site

Lead a team of AI engineers and MLOps specialists to ensure scalable, secure, and compliant AI systems across the organization. DUTIES & RESPONSIBILITIES Lead the strategy, architecture, and ...

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Mlops information

See Ohio salary details

$92.8K

$145.6K

$173.2K

How much do mlops jobs pay per year?

As of Sep 2, 2026, the average yearly pay for mlops in Ohio is $145,607.00, according to ZipRecruiter salary data. Most workers in this role earn between $137,575.00 and $158,143.00 per year, depending on experience, location, and employer.

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.

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

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

Is MLOps outdated?

MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.

What is the average salary in MLOps?

The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.

What are the most commonly searched types of Mlops jobs in Ohio?

The most popular types of Mlops jobs in Ohio are:

What are popular job titles related to Mlops jobs in Ohio?

For Mlops jobs in Ohio, the most frequently searched job titles are:

What job categories do people searching Mlops jobs in Ohio look for?

The top searched job categories for Mlops jobs in Ohio are:

What cities in Ohio are hiring for Mlops jobs?

Cities in Ohio with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Ohio as of August 2026, with employment types broken down into 91% Full Time, 3% Part Time, 1% Temporary, and 5% Contract. Highlights an 80% Physical, 6% Hybrid, and 14% Remote job distribution, with an average salary of $145,607 per year, or $70 per hour.

MLOps Engineer (Databricks/AWS)

Inabia Solutions and Consulting, Inc.

Columbus, OH • On-site

Contractor

Re-posted 14 days ago

Job description

Overview
Inabia is seeking an MLOps Engineer (Databricks/AWS) to design, develop, and maintain end-to-end machine learning operations pipelines on Databricks running on AWS. This role demands deep hands-on expertise with Databricks Machine Learning, MLflow, and a broad suite of AWS services to build scalable, production-grade ML systems. The ideal candidate will partner closely with Data Scientists, Data Engineers, and Platform Engineering teams to productionize models and ensure robust, governed, and cost-efficient deployments.
Locations: Columbus, OH - Dallas, TX - Atlanta, GA (Onsite only)
Responsibilities
  • Design, develop, and maintain end-to-end MLOps pipelines on Databricks running on AWS.
  • Build and automate machine learning workflows covering data preparation, feature engineering, model training, evaluation, deployment, and monitoring.
  • Deploy and manage ML models using MLflow Model Registry and Databricks Model Serving.
  • Develop and maintain CI/CD pipelines for ML solutions across development, staging, and production environments.
  • Collaborate with Data Scientists to productionize machine learning models and ensure reliable, repeatable deployments.
  • Monitor model health, prediction quality, data drift, and system performance, and implement automated retraining strategies where required.
  • Optimize Databricks workloads for performance, scalability, and cost efficiency.
  • Implement Infrastructure as Code using Terraform to provision and manage Databricks and AWS resources.
  • Ensure platform security, governance, and compliance using Unity Catalog and AWS IAM.
  • Troubleshoot production issues, perform root cause analysis, and continuously improve platform reliability.
  • Document MLOps processes, deployment standards, and operational best practices.

Key Qualifications
  • 10+ years of relevant experience in MLOps or a closely related data/ML engineering discipline.
  • Strong hands-on experience with Databricks Machine Learning.
  • Proficiency in Python, PySpark, and SQL for developing and operationalizing machine learning solutions.
  • Hands-on experience with MLflow for experiment tracking, model registry, model versioning, and lifecycle management.
  • Experience deploying and managing machine learning models using Databricks Model Serving and batch inference pipelines.
  • Strong understanding of the end-to-end ML lifecycle, including feature engineering, training, validation, deployment, monitoring, and retraining.
  • Experience building scalable ML pipelines using Databricks Workflows and Delta Lake.
  • Hands-on experience with AWS services including S3, IAM, EC2, Lambda, ECR, ECS/EKS, CloudWatch, and Secrets Manager.
  • Experience implementing CI/CD pipelines for Databricks and ML workloads using Git, Bitbucket, Jenkins, and Databricks Asset Bundles (DAB).
  • Experience with infrastructure automation using Terraform.
  • Strong understanding of Apache Spark architecture, optimization, and distributed data processing.
  • Experience working with Unity Catalog for governance, security, and access management.
  • Knowledge of model monitoring, data drift detection, and automated retraining strategies.
  • Understanding of MLOps best practices including reproducibility, versioning, testing, and governance.
  • Excellent analytical, problem-solving, and communication skills.

Preferred Qualifications
  • Experience with multi-environment Databricks deployments spanning development, staging, and production.
  • Familiarity with cost optimization strategies for large-scale Databricks and AWS workloads.
  • Prior experience documenting MLOps operational standards for cross-functional teams.

Apply