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Mlops Machine Learning Engineer Jobs in Virginia

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site +1

We are seeking a Machine Learning Engineer with a passion for building mission-critical ... We embrace modern agile practices, a DataOps/DevSecOps/MLOps ethos to "automate-first," and modern ...

NT Concepts

Machine Learning Engineer

Chantilly, VA · On-site

$120K - $180K/yr

We are seeking a Machine Learning Engineer with a passion for building mission-critical ... MLOps & Pipeline Automation : Build, maintain, and optimize robust pipelines for data preparation ...

WorkNovas LLC

Machine Learning Engineer

Richmond, VA · On-site

Machine Learning Engineer Richmond, Virginia (5 Days Onsite) need local within commute About the ... MLOps, or AI observability Understanding of security, identity, and compliance in enterprise AI ...

Unissant

Machine Learning Engineer

Ashburn, VA · On-site

$125 - $150/hr

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA ... Familiarity with MLOps practices, CI/CD pipelines, and model deployment processes. * Working ...

UNISSANT

Machine Learning Engineer

Ashburn, VA · On-site

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA ... Familiarity with MLOps practices, CI/CD pipelines, and model deployment processes. * Working ...

Unissant

Machine Learning Engineer

Ashburn, VA · On-site

$112K - $177K/yr

We are seeking a Machine Learning Engineer to join our team and support our client in Ashburn, VA ... Familiarity with MLOps practices, CI/CD pipelines, and model deployment processes.Working knowledge ...

CACI

Machine Learning Engineer

Chantilly, VA · On-site

$131K - $290K/yr

Machine Learning Engineer Job Category: Information Technology Time Type: Full time Minimum ... knowledge of MLOps practices for ML model deployment and monitoring Experience with container ...

Tetra Tech

Machine Learning Engineer - Remote

Vienna, VA · On-site

$150 - $200/hr

Deployment & MLOps * Operationalize models with robust CI/CD workflows. * Deploy models usingMLflow ... Required Skills: * 5+ years of experience in ML Engineering or Applied Machine Learning. * Strong ...

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Mlops Machine Learning Engineer information

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.

What are the key skills and qualifications needed to thrive as an MLOps machine learning engineer?

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.

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 machine learning engineers in demand?

MLOps machine learning engineers are in high demand due to the increasing adoption of AI and machine learning across industries. They are needed to develop, deploy, and maintain scalable ML systems, often requiring skills in cloud platforms, automation, and tools like Docker and Kubernetes. The role offers strong job growth prospects and competitive salaries.

Do MLOps Machine Learning Engineers need a degree?

MLOps Machine Learning Engineers typically do not require a formal degree but often have a background in computer science, data science, or related fields. Practical skills in machine learning, cloud platforms, and tools like Docker, Kubernetes, and CI/CD pipelines are highly valued. Certifications and hands-on experience can also enhance job prospects.

What are popular job titles related to Mlops Machine Learning Engineer jobs in Virginia?

For Mlops Machine Learning Engineer jobs in Virginia, the most frequently searched job titles are:

What job categories do people searching Mlops Machine Learning Engineer jobs in Virginia look for?

The top searched job categories for Mlops Machine Learning Engineer jobs in Virginia are:

What cities in Virginia are hiring for Mlops Machine Learning Engineer jobs?

Cities in Virginia with the most Mlops Machine Learning Engineer job openings:

Mlops Machine Learning Engineer jobs near you
Infographic showing various Mlops Machine Learning Engineer job openings in Virginia as of September 2026, with employment types broken down into 1% Internship, 1% As Needed, 71% Full Time, 24% Part Time, 2% Contract, and 1% Nights. Highlights an 82% Physical, 3% Hybrid, and 15% Remote job distribution.

Machine Learning Engineer - LLM / MLOps

HRC Global Services

Reston, VA • On-site

Full-time

Re-posted 24 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