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

As of Jul 5, 2026, the average yearly pay for machine learning operations engineer in the United States is $85,029.00, according to ZipRecruiter salary data. Most workers in this role earn between $69,500.00 and $94,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a Machine Learning Operations Engineer, and why are they important?

To thrive as a Machine Learning Operations Engineer, you need a strong background in computer science, machine learning principles, and software engineering, typically with a bachelor's or master's degree in a related field. Familiarity with cloud platforms (like AWS, GCP, or Azure), containerization tools (such as Docker and Kubernetes), and CI/CD pipelines, as well as experience with MLOps frameworks (like MLflow or Kubeflow), is essential. Excellent problem-solving, collaboration, and communication skills help bridge the gap between data science and IT teams. These skills ensure efficient deployment, monitoring, and scaling of ML models, enabling reliable and maintainable AI solutions in production environments.

How does a Machine Learning Operations Engineer typically collaborate with data scientists and software engineers on production projects?

Machine Learning Operations Engineers play a crucial role in bridging the gap between data scientists, who develop models, and software engineers, who deploy applications. They work closely with data scientists to understand the requirements and constraints of ML models, ensuring smooth transition from prototype to production. MLOps Engineers also collaborate with software engineers to integrate models into scalable, reliable systems while managing version control, monitoring, and continuous delivery pipelines. Effective communication and cross-functional teamwork are essential to address challenges like model drift, resource allocation, and deployment automation.

What is a Machine Learning Operations Engineer?

A Machine Learning Operations (MLOps) Engineer is a professional who specializes in deploying, managing, and maintaining machine learning models in production environments. They bridge the gap between data science and IT operations, ensuring that machine learning solutions are scalable, reliable, and efficient. MLOps Engineers automate workflows, monitor model performance, and address issues related to model versioning, data drift, and system integration. Their work is crucial for enabling organizations to leverage AI at scale while maintaining compliance and reliability.
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Machine Learning Operations Engineer jobs near you
Infographic showing various Machine Learning Operations Engineer job openings in the United States as of June 2026, with employment types broken down into 78% Full Time, 20% Part Time, 1% Temporary, and 1% Contract. Highlights an 87% Physical, 2% Hybrid, and 11% Remote job distribution, with an average salary of $85,029 per year, or $40.9 per hour.
Senior Python & Machine Learning Operations Engineer

Senior Python & Machine Learning Operations Engineer

Software Technology Inc

Washington, DC • On-site

Other

Posted 5 days ago

Job description

Senior Python/ML Operations Engineer

We are seeking a knowledgeable Senior Python/ML Operations Engineer with advanced Python and Flask, Large Language Models, Open API engineering, Containerization and Swagger expertise for a multi-year engagement to work with a foremost Healthcare IT Solutions Group in their Innovations Project Team. Prerequisites for the selection of the Consultant encompass:

  • Advanced Python programming for back-end development for Machine Learning Operations.
  • Expertise with OpenAPI specs.
  • Conversant with LLMs, specifically Llama 2 or Mistral.
  • Required to productionize Large Language Model (LLM) based solutions.
  • Capable of taking an open-source Large Language Model (LLM) and fine tuning it to reflect custom data and retrieve data from a Vector database.
  • Will be required to design a RESTful API that interfaces with a Large Language Model (LLM) and can be used for user consumption.
  • Write Helm charts and the web front end.
  • Must be skilled in modularizing machine learning code.
  • Will transform Data Scientists' models into scalable, maintainable systems.
  • Integrate the APIs with MongoDB (Reddis, Vector, databases, embedding models).
  • Should have expertise in deploying and managing containerized applications using Kubernetes.
  • Ability to take an open-source hugging face model and productionalize it so that user can use it on custom data.
  • Plan the builds with Swagger CodeGen, Editor and Inspector.
  • Experience with Jupyter notebooks, Flask, FastAPI, and on premises Docker and Kubernetes.
  • Angular and Node are also utilized in this environment.
  • Document plan and steps for development.

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