ZipRecruiter

Log In

1

Machine Learning Ops Engineer Jobs (NOW HIRING)

Sralab

Engineer I, Machine Learning Ops CBM Lab

Chicago, IL · On-site

$60K - $99K/yr

Summary The Machine Learning Ops Engineer I, under direct supervision, will assist in the development, deployment, and management of machine learning models, toolboxes and systems. This role involves ...

Hayden AI

Sr. Machine Learning Ops Engineer

San Francisco, CA · On-site

$123K - $169K/yr

As a Senior MLOps Engineer within the Perception Deep Learning team, you will lead the design and evolution of our machine learning platform, enabling teams to build, deploy, and scale intelligent ...

RELX

Senior ML Ops Engineer

Philadelphia, PA · On-site

$99K - $137K/yr

As a Senior Machine Learning Ops Engineer, you will bridge Data Science and Engineering to develop AI-based features and ensure the reliability and scalability of machine learning models and services.

Moody's Analytics

MLOps Engineer

New York, NY · On-site +1

As the Machine Learning Ops Engineer for the AI Team you will: * Work closely with the Data Science team and the Data Engineers and DevOps teams in order to deploy machine learning models.

Imh

$51.25 - $70.25/hr

Machine Learning Ops Engineers are responsible for working closely with our data scientists, data architects / engineers, and software engineers to build and deploy machine learning solutions that ...

New

next page

Showing results 1-20

All JobsMachine Learning Ops Engineer Jobs

Machine Learning OPS Engineer information

See salary details

$31.5K

$128.8K

$193.5K

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

As of Aug 21, 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?

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 machine learning ops engineer do?

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 skills and qualifications are needed to be a machine learning ops engineer?

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.

Are machine learning ops engineers in demand?

Machine Learning Ops Engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Kubernetes and TensorFlow. The role is expected to grow as organizations prioritize AI-driven solutions and infrastructure automation.
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:

What job categories do people searching Machine Learning Ops Engineer jobs look for?

The top searched job categories for Machine Learning Ops Engineer jobs are:

Machine Learning OPS Engineer jobs near you
Infographic showing various Machine Learning Ops Engineer job openings in the United States as of August 2026, with employment types broken down into 1% As Needed, 75% Full Time, 23% Part Time, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Ops Engineer

Ruri Software Technologies LLC

Remote

$54 - $74/hr

Full-time

This job post has expired 1 day ago. Applications are no longer accepted.

Job description

Job Title:- Machine Learning Ops Engineer
Duration:- 8+ months
Location:- Remote
Description
  • Proven expertise in machine learning model lifecycleProven expertise and experience in creating data pipelines using Python or R required for real-time model inferenceProven expertise and experience in creating a microservice using Flask or FastAPI or R equivalent for a machine learning model.
  • Experience with APIGEE is a must. Proven experience with Linux bash scripting, Python scripting, or Groovy scripting Proven experience with Docker, and KubernetesProven experience with MPP databases like Teradata and reasonable experience with Hadoop ecosystem products like Hive, HDFS, HBASE, etc.
  • Proven experience with streaming technologies like KafkaProven experience with CICD tools like Jenkins, Shell Scripting, Gitlab, Github, Gitlab Pages, and Gitlab Documentation Proven experience with logging, alerting, debugging, and monitoring tools like ELK, Kibana, Catchpoint, Prometheus, and Splunk. Experience with EKS, or GKE is a plus

Apply