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Machine Learning Ops Engineer Jobs (NOW HIRING)

Sr. Machine Learning Ops Engineer

Los Angeles, CA · On-site

$140K - $179K/yr

They are seeking a Senior Machine Learning Ops Engineer to lead the design and maintenance of scalable infrastructure for ML model deployment and lifecycle management, with a focus on productionizing ...

Senior Machine Learning Ops Engineer

$150K - $173K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

... Engineer to help evolve and scale our enterprise machine learning platform. This role sits within ... The ideal candidate combines strong software engineering fundamentals with hands-on Ops experience ...

Sr. Machine Learning Ops Engineer

Los Angeles, CA · On-site

$112K - $154K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

  • PTO

The Senior ML Ops Engineer leads the design and maintenance of scalable, secure infrastructure for ... scalability and robust governance of machine learning and generative AI initiatives.

Sr. Machine Learning Ops Engineer

Los Angeles, CA · On-site

$112K - $154K/yr

CIM Group is a community-focused real estate and infrastructure company seeking a Senior ML Ops Engineer to lead the design and maintenance of scalable infrastructure for ML model deployment and ...

Sr. Machine Learning Ops Engineer

Los Angeles, CA · On-site

$112K - $154K/yr

They are seeking a Senior ML Ops Engineer to lead the design and maintenance of scalable infrastructure for ML model deployment and lifecycle management, with a focus on Generative AI solutions.

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.

ML Ops Engineer Concord CA Contract Cleint Round - Inperson Overview Tachyon Cortex Machine Learning AI team seeking a ML Ops Engineer to drive the full lifecycle of machine learning solutions. Key ...

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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 Aug 14, 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.

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:

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, 73% Full Time, 23% Part Time, 1% Temporary, and 2% Contract. Highlights an 88% Physical, 2% Hybrid, and 10% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

$54 - $74/hr

Full-time

Re-posted 26 days ago


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