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Mlops Machine Learning Engineer Jobs (NOW HIRING)

Constellation Space

Machine Learning Engineer

Seattle, WA · On-site

$120K - $180K/yr

The Role We are looking for a Machine Learning Engineer to bridge the gap between AI research and ... Build robust MLOps pipelines for continuous training and integration of models using telemetry data.

AI Squared

Machine Learning Engineer

Washington, DC

Qualifications: * 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role. * Proven experience deploying and maintaining machine learning models in production at scale.

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Qualifications: * 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role. * Proven experience deploying and maintaining machine learning models in production at scale.

AI Squared

Machine Learning Engineer

Washington, DC · On-site

Qualifications: * 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role. * Proven experience deploying and maintaining machine learning models in production at scale.

Compunnel

Machine Learning Engineer

Philadelphia, PA · On-site

JOB SUMMARY Seeking a hands-on Machine Learning Engineer with strong Python programming expertise ... Local LLMs MLOps experience Distributed data processing experience Model optimization and ...

X4 Engineering

Machine Learning Engineer

Austin, TX · On-site

$140K - $180K/yr

Python | AWS | Kubernetes | Docker | Terraform | Airflow | Spark | MLflow | Databricks | Kafka | CI/CD We're interested in people from backgrounds such as: ✔ Machine Learning Engineering ✔ MLOps ...

exacare ai

Machine Learning Engineer

Manhattan, NY · On-site

They are seeking a Machine Learning Engineer focused on MLOps to operationalize and scale their machine learning systems, working closely with research-oriented teammates to create reliable ...

AISquared

Machine Learning Engineer

Washington, DC · On-site

Required : • 5+ years of experience as a Machine Learning Engineer, MLOps Engineer, or similar role. • Proven experience deploying and maintaining machine learning models in production at scale ...

Pattern AI

Machine Learning Engineer

San Mateo, CA

Manage MLOps infrastructure to monitor and optimize models. Qualifications Experience: * 3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist.

Pattern AI

Machine Learning Engineer

San Mateo, CA · On-site +1

Manage MLOps infrastructure to monitor and optimize models. Qualifications Experience: * 3+ years of professional experience as a Machine Learning Engineer or production-focused Data Scientist.

Chabez Tech

Machine Learning Engineer

Portland, OR · On-site

Job Title: Machine Learning Engineer Location: Portland, OR - Onsite (Local only / F2F interview ... MLOps practices (CI/CD, monitoring, model governance) Experience working in air-gapped or high ...

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All JobsMlops Machine Learning Engineer Jobs

Mlops Machine Learning Engineer information

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$31.5K

$128.8K

$193.5K

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

As of Jul 7, 2026, the average yearly pay for mlops machine learning 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 does an MLOps Machine Learning Engineer do?

An MLOps Machine Learning Engineer bridges the gap between data science and IT operations by developing, deploying, and maintaining machine learning models in production environments. They are responsible for automating workflows, managing model versioning, monitoring performance, and ensuring scalability and reliability of ML systems. Their work enables organizations to deploy machine learning solutions efficiently and consistently, making it easier to update and manage models as business needs evolve.

How does an MLOps Machine Learning Engineer typically collaborate with data scientists and software engineers during the deployment of machine learning models?

An MLOps Machine Learning Engineer acts as a bridge between data scientists and software engineers, ensuring machine learning models transition smoothly from development to production. They often work closely with data scientists to understand model requirements, data pipelines, and performance metrics, while also collaborating with software engineers to integrate models into scalable systems. Regular communication, shared documentation, and joint troubleshooting sessions are common, as the role requires aligning model performance with system reliability and maintainability. This collaborative environment helps ensure that models are robust, scalable, and impactful in real-world applications.

What is the difference between Mlops Machine Learning Engineer vs Data Scientist?

AspectMlops Machine Learning EngineerData Scientist
Required CredentialsBachelor's or master's in CS, data science, or related fields; certifications in cloud platforms or MLOps toolsBachelor's or master's in statistics, data science, or related fields; certifications in data analysis or machine learning
Work EnvironmentFocus on deploying, maintaining, and scaling ML models in production environmentsFocus on data analysis, model development, and insights generation
Employer & Industry UsageTech companies, startups, enterprises implementing ML solutionsResearch institutions, analytics firms, tech companies for data insights

While both roles involve machine learning, Mlops Machine Learning Engineers specialize in deploying and maintaining models in production, ensuring scalability and reliability. Data Scientists primarily focus on developing models and analyzing data to generate insights. The roles often overlap but differ in their core responsibilities and work environments.

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

To thrive as an MLOps Machine Learning Engineer, you need a strong background in machine learning concepts, software engineering, and cloud infrastructure, typically supported by a degree in computer science or a related field. Familiarity with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (AWS, GCP, Azure), and certifications such as Google Professional Machine Learning Engineer are highly beneficial. Strong problem-solving abilities, collaboration, and communication skills help you work effectively across data science and engineering teams. These skills are essential for reliably deploying, monitoring, and maintaining scalable machine learning solutions in production environments.
More about Mlops Machine Learning Engineer jobs
What cities are hiring for Mlops Machine Learning Engineer jobs? Cities with the most Mlops Machine Learning Engineer job openings:
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Mlops Machine Learning Engineer jobs near you
Infographic showing various Mlops Machine Learning Engineer job openings in the United States as of July 2026, with employment types broken down into 95% Full Time, 2% Part Time, and 3% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $128,769 per year, or $61.9 per hour.

Machine Learning Engineer - LLM / MLOps

HRC Global Services

Reston, VA • On-site

Full-time

Re-posted 21 days ago

Job description

Machine Learning Engineer – LLM / MLOps

Job Title: Machine Learning Engineer – LLM & MLOps
Location: Remote (U.S.)
Employment Type: Full-Time

About the Opportunity:
An exciting role for an ML Engineer to build scalable ML systems, deploy models, and work with cutting-edge AI technologies including LLMs and RAG architectures.

Key Responsibilities:

  • Build, train, and deploy ML models at scale
  • Develop reusable pipelines using Databricks and MLflow
  • Implement CI/CD workflows for ML deployment
  • Work with LLMs, RAG, and AI agent frameworks
  • Monitor model performance, drift, and retraining cycles

Required Skills:

  • 5+ years of ML Engineering experience
  • Strong Python programming and ML frameworks (PyTorch, TensorFlow, Scikit-learn)
  • Hands-on experience with Databricks, MLflow, PySpark
  • Experience with AWS (S3, SageMaker, Lambda, etc.)
  • Strong understanding of MLOps and model lifecycle

Preferred:

  • Experience building AI-driven applications (Streamlit, Gradio)
  • Strong system design and data pipeline experience
  • Business understanding of AI applications

Clearance: Public Trust (or eligible)

Hashtags:
#MLEngineer #MachineLearning #MLOps #LLM #AWS #Databricks #PySpark #AIEngineering #RemoteJobs #HiringNow