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On Call Machine Learning Ops Engineer Jobs (NOW HIRING)

Machine Learning AI team seeking a ML Ops Engineer to drive the full lifecycle of machine learning solutions. Key Responsibilities * Develop and maintain ML pipelines using tools like MLflow ...

New

Role Description As the first ML Ops Engineer at Tennr, you'll play a crucial role in building and iterating on foundational Machine Learning and AI systems. You'll own building machine learning ...

Concord CA (Onsite) (In-person Interview Must) Overview Tachyon Cortex Machine Learning AI team seeking a ML Ops Engineer to drive the full lifecycle of machine learning solutions. Key ...

Strong experience with ML Ops tooling and practices: CI/CD pipelines for model code and artifacts ... Machine Learning, Artificial Intelligence, Engineering, or a related field. • 3+ years of ...

Senior ML Ops Engineer

Dallas, TX

$103.80K - $142.60K/yr

Hello Senior ML Ops Engineer Dallas, TX (Onsite) Long Term Contract Client will be discussed during ... Machine Learning Algorithms. * c. Statistical Modeling. * d. End to end deployment. * e. Metric ...

Senior Software Engineer

Olathe, KS · On-site

$118.60K - $156.40K/yr

Designs, develops, and maintains self-service Machine Learning Ops tooling, platforms, and ... Supports working hours as part of a rotating schedule to provide on call support of Garmin's 24/7 ...

Machine Learning Engineer

New York, NY · On-site +1

$170K - $212K/yr

Implement and monitor model success metrics, diagnose issues, and contribute to an on-call schedule ... machine learning engineers, and data engineers to innovate and improve models. * You have ...

Machine Learning Engineer

New York, NY · On-site +1

$170K - $212K/yr

Implement and monitor model success metrics, diagnose issues, and contribute to an on-call schedule ... machine learning engineers, and data engineers to innovate and improve models. * You have ...

Adobe is looking for a Senior Machine Learning Engineer to help shape the future of agentic AI in ... Ops best practices, delivering high quality, production ready code. • Design and build ML ...

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On Call Machine Learning Ops Engineer information

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$31.5K

$128.8K

$193.5K

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

As of May 30, 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.
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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:
MLOps Engineer

Other

Posted 2 days ago


Job description

Machine Learning AI team seeking a ML Ops Engineer to drive the full lifecycle of machine learning solutions.

Key Responsibilities

  • Develop and maintain ML pipelines using tools like MLflow, Kubeflow, or Vertex AI.
  • Automate model training, testing, deployment, and monitoring in cloud environments (e.g., Google Cloud Platform, AWS, Azure).
  • Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
  • Monitor model performance using observability tools and ensure compliance with model governance frameworks (MRM, documentation, explainability)
  • Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs
  • Leverage AutoML tools (e.g., Vertex AI AutoML, H2O Driverless AI) for low-code/no-code model development, documentation automation, and rapid deployment

Qualifications

  • 10+ Years of professional experience in Software Engineering & 3+ Years in AIML, Machine Learning Model Operations.
  • Strong proficiency in Java and Python, SQL, and ML libraries (e.g., scikit-learn, XGBoost, TensorFlow, PyTorch).
  • Experience with cloud platforms and containerization (Docker, Kubernetes).
  • Familiarity with data engineering tools (e.g., Airflow, Spark) and ML Ops frameworks.
  • Solid understanding of software engineering principles and DevOps practices.
  • Ability to communicate complex technical concepts to non-technical stakeholders.