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Mlops Jobs in Ohio (NOW HIRING)

Flexjet LLC

Director of AI Engineering

Cleveland, OH · On-site

$180 - $260/hr

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 ...

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 ...

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 ...

ClifyX

Machine Learning Engineer

Dayton, OH · On-site

The role involves designing advanced ML models, managing MLOps pipelines, and collaborating with teams to deliver production-ready AI solutions. Responsibilities : • Design advanced ML models ...

Mindlance

Machine Learning Engineer

Cincinnati, OH · On-site

Design and maintain ML/AI pipelines and MLOps processes across the model lifecycle. * Work with development and data teams to understand business problems, data sources, and existing systems and ...

Sabio infotech

Sr. Machine Learning Engineer

Cincinnati, OH · On-site

$100K - $137K/yr

... MLOps, model lifecycle management, monitoring, validation, version control, auditability, and AI governance. · Collaborate with Risk, Compliance, Information Security, and business partners to ...

Incedo Inc

Senior Machine Learning Engineer

Cincinnati, OH · On-site

$100K - $137K/yr

Establish and maintain frameworks for MLOps, model lifecycle management, monitoring, validation, version control, auditability, and AI governance . * Collaborate with Risk, Compliance, Information ...

CONET Technologies AG

$69.66 - $98.69/hr

Weiterentwicklung der Geschäftsfelder GenAI und MLOps gemeinsam mit dem Team Das bringst du mit * erfolgreich abgeschlossenes Studium in einem MINT‑Fach oder eine vergleichbare Qualifikation * ...

Hudson Manpower

Data Scientist

Cincinnati, OH · On-site

$55 - $60/hr

Build scalable, production-ready ML pipelines using software engineering and MLOps best practices. * Partner with business and product teams to convert business problems into scientific solutions.

Penske

Staff Data Scientist

Beachwood, OH · On-site +1

Ensure solutions are production ready, maintainable, and aligned with MLOps best practices. * Drive organization wide adoption of models and AI systems through clear communication, documentation, and ...

Penske

Staff Data Scientist

Beachwood, OH · On-site

Ensure solutions are production ready, maintainable, and aligned with MLOps best practices. * Drive organization wide adoption of models and AI systems through clear communication, documentation, and ...

Penske

Staff Data Scientist

Beachwood, OH · On-site +1

Ensure solutions are production ready, maintainable, and aligned with MLOps best practices. * Drive organization wide adoption of models and AI systems through clear communication, documentation, and ...

Apex Systems

AI Engineer

Cleveland, OH · On-site

Familiarity with MLOps tools (MLflow, Docker, Kubernetes, GitHub Actions). EEO Employer Apex Systems is an equal opportunity employer. We do not discriminate or allow discrimination on the basis of ...

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All JobsMlops JobsMlops Jobs in Ohio

Mlops information

See Ohio salary details

$92.8K

$145.6K

$173.2K

How much do mlops jobs pay per year?

As of Sep 5, 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.

Director of AI Engineering

Flexjet LLC

Cleveland, OH • On-site

$180 - $260/hr

Other

Re-posted 18 hours ago

Flexjet rating

8.2

Company rating: 8.2 out of 10

Based on 24 frontline employees who took The Breakroom Quiz

19th of 67 rated aviation services

Job description

Current job opportunities are posted here as they become available.

Flexjet is seeking a Director of AI Engineering to lead the design, deployment, and operationalization of enterprise‑scale machine learning and generative AI systems. This role is responsible for building and managing the infrastructure, systems, and processes required to reliably deploy and maintain AI solutions in production. Combine strong engineering leadership with deep expertise in MLOps, cloud infrastructure, model lifecycle management, and Generative AI deployment. 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 implementation of enterprise AI, Generative AI, and MLOps platforms while establishing standards for model development, deployment, monitoring, governance, and lifecycle management.
  • Design and scale cloud‑native AI infrastructure, including distributed compute environments, containerized platforms, CI/CD pipelines, and cost‑optimized ML operations.
  • Oversee the production deployment of machine learning and LLM‑powered applications, including RAG solutions, AI copilots, model evaluation frameworks, guardrails, and automated retraining processes.
  • Ensure compliance with responsible AI, security, risk management, data privacy, auditability, reproducibility, documentation, and regulatory requirements.
  • Build and manage reusable AI platform services and frameworks that support multiple data science and engineering teams.
  • Lead, mentor, and grow teams of AI Engineers and MLOps Engineers, fostering engineering excellence, innovation, talent development, and performance accountability.
  • Partner with Data Scientists, Software Engineering, Security, DevOps, and Product leadership teams to drive enterprise AI adoption and align technical strategy with business objectives.
  • Communicate AI platform vision, roadmap, and operational performance to executive stakeholders.
EDUCATION & EXPERIENCE
  • Bachelor’s or Master’s degree in Computer Science, Information Technology, or a related field, or an equivalent combination of education, training, and relevant professional experience.
  • 10+ years of experience in software engineering, machine learning engineering, platform engineering, MLOps, or DevOps.
  • 5+ years of leadership experience managing and mentoring technical teams in fast‑paced, technology‑driven environments.
  • Experience implementing and deploying complex and integrated information systems.
  • Proven experience in leading application development teams in an enterprise environment.
  • Experience working with Agile methodology.
  • Experience in managing large projects including setting deadlines, identifying interdependencies, communicating with stakeholders, gathering requirements, and setting expectations.
REQUIRED TECHNICAL SKILLS & QUALIFICATIONS
  • Strong experience with MLOps and platform engineering, including model lifecycle management, CI/CD, model versioning, feature stores, experiment tracking, and automated retraining pipelines.
  • Proficiency with cloud and infrastructure technologies, including AWS, Azure, or Google Cloud Platform (GCP), Kubernetes, Docker, Terraform, and distributed systems.
  • Expertise in machine learning systems, including model deployment, monitoring and observability, data pipelines, and real‑time inference architectures.
  • Experience with Generative AI and LLM technologies, including LLM deployment, Retrieval‑Augmented Generation (RAG), prompt orchestration, model governance and guardrails, and cost optimization strategies.
  • Strong programming skills in Python, SQL, Bash, Git, and CI/CD tools.
PREFFERED QUALIFICATIONS
  • Experience deploying Generative AI and LLM solutions in large‑scale enterprise environments.
  • Experience designing and supporting multi‑tenant AI/ML platforms.
  • Familiarity with RAG architectures, vector databases, and LLM evaluation frameworks.
  • Experience managing GPU infrastructure and distributed training workloads.
  • Knowledge of AI security, governance, risk management, and regulatory compliance frameworks.
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