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Machine Learning Ops Engineer Jobs (NOW HIRING)

Elsevier

Senior ML Ops Engineer

Philadelphia, PA

$112K - $179K/yr

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global organization? Are you looking to drive cutting edge products that have a true societal impact? About ...

RELX Group plc

Senior ML Ops Engineer

Philadelphia, PA · On-site

$112K - $179K/yr

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global organization? Are you looking to drive cutting edge products that have a true societal impact? About ...

LMI

ML Ops Engineer - Clearance Required

Overview LMI is seeking a Machine Learning Operations Engineer (ML Ops Engineer) to support the development of cutting-edge AI/ML solutions in collaboration with the Army's AI2C organization. This ...

LMI

Overview LMI is seeking a Machine Learning Operations Engineer (ML Ops Engineer) to support the development of cutting-edge AI/ML solutions in collaboration with the Army's AI2C organization. This ...

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Machine Learning/AI Engineer Location: Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and ... Knowledge of Machine Learning Ops and CI/CD tools for automation of build, test, and deploy models ...

LMI

Overview LMI is seeking a Machine Learning Operations Engineer (ML Ops Engineer) to support the development of cutting-edge AI/ML solutions in collaboration with the Army's AI2C organization. This ...

Donato Technologies, Inc

ML Ops Engineer

Charlotte, NC · On-site

$60/hr

Overview 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 ...

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Machine Learning/AI Engineer Location: Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and ... Knowledge of Machine Learning Ops and CI/CD tools for automation of build, test, and deploy models ...

RELX

Senior ML Ops Engineer

Philadelphia, PA

$112K - $179K/yr

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global organization? Are you looking to drive cutting edge products that have a true societal impact? About ...

Elsevier

Senior ML Ops Engineer

Philadelphia, PA · On-site

$112K - $179K/yr

Are you a collaborative Machine Learning Ops Engineer looking to work for a mission driven global organization? Are you looking to drive cutting edge products that have a true societal impact? About ...

LMI

ML Ops Engineer - Clearance Required

Overview LMI is seeking a Machine Learning Operations Engineer (ML Ops Engineer) to support the development of cutting-edge AI/ML solutions in collaboration with the Army's AI2C organization. This ...

System One

Machine Learning/AI Engineer

Merrifield, VA · Remote

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Machine Learning/AI Engineer Location: Hybrid in Vienna, VA or Remote Pay Rate: Open to Both W2 and ... Knowledge of Machine Learning Ops and CI/CD tools for automation of build, test, and deploy models ...

TikTok

Machine Learning Ops Engineer, Global SRE

San Jose, CA · On-site

$244K - $450K/yr

  • Medical

  • Dental

  • Vision

  • Life

  • Retirement

Responsibilities MLOps - Global SRE team is responsible for the stability of machine learning systems under the Global Monetization Products and Technology organization, to ensure the stable and ...

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Showing results 21-40

All JobsMachine Learning Ops Engineer Jobs

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.

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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, 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.

Hiring: ML-Ops Engineer at Concord, CA

Realtech Services

Concord, CA • On-site

Contractor

Re-posted 2 days ago

Job description


 
 

Job Title: ML-Ops Engineer

Location: Concord, CA (Onsite)

Duration: Long-Term Contract

Interview Process: Client Round – In-person (Lets Target only locals and willing to go for in-person interview at Client’s location)

Overview:

  • Tachyon Cortex 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., GCP, 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 Auto ML tools (e.g., Vertex AI Auto ML, 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.