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Mlops Machine Learning Engineer Jobs in Washington

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

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

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.

Ametek

Machine Learning Engineer

Herndon, VA · Hybrid

We are seeking an earlycareer Machine Learning Engineer who is excited to grow rapidly by building ... Work with DevOps team to implement robust MLOps practices, including versioning, CI/CD for ML ...

10a Labs

Machine Learning Engineer

Washington, DC · On-site +1

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ... Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site +1

We follow modern agile practices, embrace the Ops ethos (DataOps/DevSecOps/MLOps) to "automate ... As a Machine Learning Engineer, you'll be part of the Agile team delivering machine learning ...

Ametek

Machine Learning Engineer

Herndon, VA · On-site

We are seeking an early-career Machine Learning Engineer who is excited to grow rapidly by building ... Work with DevOps team to implement robust MLOps practices, including versioning, CI/CD for ML ...

10a Labs

Machine Learning Engineer

Washington, DC · On-site

$130K - $200K/yr

We are seeking a Machine Learning Engineer (3-5+ years of experience) to help design, build ... Experience with MLOps tools and practices (Docker, Kubernetes, CI/CD for ML, MLflow, etc.

Spear AI

Machine Learning Engineer

Washington, DC · On-site +1

We're seeking a skilled Machine Learning Engineer to build and deploy production ML systems for the ... Proficiency with MLOps practices including experiment tracking, model versioning, and deployment

Full Scope

Machine Learning Engineer

Reston, VA

Machine Learning Engineer Location: Fort Meade, MD Required Clearance : TS/SCI w/ Full-Scope Poly Salary: Competitive We are seeking a highly skilled and motivated Machine Learning Engineer to join ...

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Mlops Machine Learning Engineer information

Is MLOps harder than DevOps?

MLOps, as a specialized subset of DevOps focused on deploying and maintaining machine learning models, often involves additional challenges such as data management, model versioning, and monitoring. While both require skills in automation, scripting, and cloud environments, MLOps typically demands expertise in machine learning workflows and tools like TensorFlow or PyTorch, making it more complex in certain aspects compared to traditional DevOps.

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.

Are MLOps engineers in demand?

MLOps engineers are in high demand due to the increasing adoption of machine learning models in various industries. Their skills in deploying, managing, and scaling machine learning systems, along with knowledge of tools like Docker, Kubernetes, and cloud platforms, make them valuable in the job market.

What engineers make $500,000?

Senior machine learning engineers, including those specializing in MLOps, often reach or exceed $500,000 annually with experience, advanced skills, and in high-demand industries like tech or finance. Compensation can include base salary, bonuses, and stock options, especially at large tech companies or startups with significant funding.

How much do MLOps engineers make?

MLOps engineers typically earn between $100,000 and $150,000 annually, with salaries increasing based on experience, location, and expertise in tools like Kubernetes, Docker, and cloud platforms. Senior roles or those with specialized skills can exceed $180,000 per year.

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.
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Mlops Machine Learning Engineer jobs near you

Machine Learning Engineer - LLM / MLOps

HRC Global Services

Reston, VA • On-site

Full-time

Posted 11 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