1

On Call Machine Learning Ops Engineer Jobs (NOW HIRING)

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 ...

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 ...

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.

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.

$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 ...

Posted today

next page

Showing results 1-20

On Call Machine Learning Ops Engineer information

See salary details

$31.5K

$128.8K

$193.5K

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

As of Aug 19, 2026, the average yearly pay for on call 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.

Are on call machine learning ops engineers in demand?

On call machine learning ops engineers are in high demand due to the increasing reliance on AI and machine learning systems in various industries. Their skills in deploying, monitoring, and maintaining ML models using tools like Kubernetes and cloud platforms are highly sought after, especially in organizations prioritizing scalable and reliable AI solutions.

How much do on call machine learning ops engineers make in the US?

On-call machine learning operations (MLOps) engineers in the US typically earn between $100,000 and $150,000 annually, depending on experience, location, and company size. Salaries can increase with expertise in cloud platforms, automation tools, and monitoring systems, and may include additional compensation for on-call duties and overtime.
More about On Call Machine Learning Ops Engineer jobs

What cities are hiring for On Call Machine Learning Ops Engineer jobs?

Cities with the most On Call 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:

Infographic showing various On Call 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.

$54 - $74/hr

Full-time

Re-posted yesterday


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