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Machine Learning Ops Engineer Jobs (NOW HIRING)

Chase

Applied AI ML Lead - ML Ops, CTC

Columbus, OH

ML Ops Engineer Step into a fast-growing area of Cybersecurity at JPMorganChase, where you can help build and deploy machine learning solutions that directly support Cyber Operations. In this role ...

Chase

Applied AI ML Lead - ML Ops, CTC

Houston, TX · On-site

ML Ops Engineer Step into a fast-growing area of Cybersecurity at JPMorganChase, where you can help build and deploy machine learning solutions that directly support Cyber Operations. In this role ...

iHerb

OR

$134K - $180K/yr

... Ops engineer, or related position). Education Requirements: Bachelor's Degree in Computer Science, Electrical Engineering, or related field required, Masters Degree preferred. Judgment/Reasoning ...

iHerb

Principal Machine Learning Engineer

$138K - $185K/yr

... Ops engineer, or related position). Education Requirements: Bachelor's Degree in Computer Science, Electrical Engineering, or related field required, Masters Degree preferred. Judgment/Reasoning ...

My3Tech Inc

Machine Learning Engineer

Denver, CO · On-site

MGMA is seeking a Machine Learning Engineer to enhance and expand its data ecosystem through ... Support ML Ops practices including model deployment, monitoring, and lifecycle management

Tennr

ML Infrastructure Engineer

New York, NY · On-site

$190K - $230K/yr

You'll own building machine learning training and inference pipelines that can handle increasing ... Collaborate closely with ML engineers, software engineers, and cross-functional teams to ensure ...

Chalk

Infrastructure Engineer

San Francisco, CA · On-site

$126K - $166K/yr

They are seeking exceptional engineers to join their Infrastructure team to tackle challenging ... Machine Learning Ops/Infrastructure Company : Chalk is a data platform for AI inference that ...

Mimecast

Senior ML Ops Engineer

Columbus, OH · On-site

$100K - $138K/yr

This role sits at the intersection of infrastructure engineering and machine learning. You will own ... Mentor ML Ops and ML Engineers on operational best practices. Participate in architectural reviews ...

Mimecast

Senior ML Ops Engineer

Columbus, OH

$100K - $138K/yr

This role sits at the intersection of infrastructure engineering and machine learning. You will own ... Mentor ML Ops and ML Engineers on operational best practices. Participate in architectural reviews ...

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Machine Learning Ops Engineer information

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$31.5K

$128.8K

$193.5K

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

As of Jun 11, 2026, the average yearly pay for machine learning ops engineer in the United States is $128,769.00, according to ZipRecruiter salary data. Most workers in this role earn between $101,500.00 and $155,000.00 per year, depending on experience, location, and employer.

What is a Machine Learning Ops Engineer job?

A Machine Learning Ops Engineer (MLOps Engineer) focuses on deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and software engineering, ensuring models run efficiently, reliably, and at scale. Their responsibilities include automating workflows, managing infrastructure, and ensuring CI/CD pipelines for ML models. They work with tools like Kubernetes, Docker, and cloud platforms to streamline model deployment. Ultimately, an MLOps Engineer ensures that machine learning models are operationalized and continuously improved in a real-world environment.

What does a typical day look like for a Machine Learning Ops Engineer?

A typical day for a Machine Learning Ops Engineer involves collaborating with data scientists to streamline the deployment of models, building and maintaining scalable infrastructure on cloud services, and automating workflows with CI/CD tools. You may troubleshoot issues in production environments, monitor model performance, and implement solutions for model versioning and retraining. Often, you’ll work closely with software engineers, DevOps teams, and data analysts to ensure seamless integration of machine learning solutions into products. This cross-functional role keeps you engaged with cutting-edge technology and provides opportunities to influence both technical and business outcomes.

What are the key skills and qualifications needed to thrive in the Machine Learning Ops Engineer position, and why are they important?

To thrive as a Machine Learning Ops Engineer, you need a solid grasp of machine learning concepts, cloud platforms, software engineering, and DevOps practices, typically supported by a degree in computer science or a related field. Experience with tools like Docker, Kubernetes, TensorFlow, CI/CD pipelines, and certifications such as AWS Certified Machine Learning – Specialty are highly valuable. Strong problem-solving skills, communication, and the ability to work collaboratively across data science and engineering teams set top candidates apart. These skills ensure reliable deployment, scalability, and optimization of machine learning models in production environments.

More about Machine Learning Ops Engineer jobs
What cities are hiring for Machine Learning Ops Engineer jobs? Cities with the most Machine Learning Ops Engineer job openings:
What are the most commonly searched types of Machine Learning Ops Engineer jobs? The most popular types of Machine Learning Ops Engineer jobs are:
What states have the most Machine Learning Ops Engineer jobs? States with the most job openings for Machine Learning Ops Engineer jobs include:
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Machine Learning Ops Engineer jobs near you
Infographic showing various Machine Learning Ops Engineer job openings in the United States as of June 2026, with employment types broken down into 6% Internship, 88% Full Time, 3% Contract, and 3% Nights. Highlights an 91% In-person, and 9% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.
Applied AI ML Lead - ML Ops, CTC

Applied AI ML Lead - ML Ops, CTC

Chase

Columbus, OH

Other

Posted 5 days ago

JPMorgan Chase & Co. rating

8.1

Company rating: 8.1 out of 10

Based on 468 frontline employees who took The Breakroom Quiz

46th of 141 rated banks

Job description

ML Ops Engineer

Step into a fast-growing area of Cybersecurity at JPMorganChase, where you can help build and deploy machine learning solutions that directly support Cyber Operations. In this role, you'll work independently and apply your skills in data analysis, statistics, and data engineering to deliver machine learning models that drive real business outcomes. You'll join a global Cybersecurity team, collaborating with technologists and innovators who protect our assets every day.

As an ML Ops Engineer within Corporate Sector in Cybersecurity & Tech Controls team, you will deploy, monitor, and manage machine learning models in production environments using the latest technologies. You'll automate model deployment, optimize infrastructure, and ensure AI systems perform reliably and efficiently. Your collaboration with cross-functional teams and your problem-solving skills will help drive innovation and deliver impactful AI solutions.

Job responsibilities

  • Work closely with data scientists and software engineers to integrate machine learning models into applications.
  • Deploy machine learning models into production, ensuring they are scalable, reliable, and efficient.
  • Build and maintain CI/CD pipelines to automate testing, deployment, and updates for machine learning models.
  • Manage and optimize infrastructure for running models, including cloud services, Docker, and Kubernetes.
  • Set up monitoring and logging to track model performance, detect anomalies, and ensure smooth operation.
  • Maintain version control for models and data, supporting traceability and compliance.
  • Apply security best practices and ensure models meet regulatory standards.

Required qualifications, capabilities, and skills

  • Bachelor's degree in Computer Science, Engineering, or a related field, with relevant ML Ops experience.
  • Experience deploying and managing machine learning models in production environments.
  • Skilled in building and maintaining CI/CD pipelines for machine learning workflows.
  • Proficient with cloud platforms (AWS, Google Cloud, Azure) and containerization tools (Docker, Kubernetes).
  • Familiar with monitoring and logging tools (Prometheus, Grafana, ELK Stack).
  • Advanced Python programming skills.
  • Strong problem-solving skills and attention to detail.
  • Effective communicator, able to collaborate with cross-functional teams.

Preferred qualifications, capabilities, and skills

  • Experience deploying and managing large-scale machine learning models in production.
  • Ability to monitor models in production and address performance and data quality issues.
  • Working knowledge of security best practices and compliance standards for ML systems.
  • Experience optimizing infrastructure for performance and efficiency.
  • Developed REST APIs using frameworks like Flask or FastAPI.
  • Familiarity with synthetic datasets for model training and evaluation.

What JPMorgan Chase & Co. employees say

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