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Mlops Jobs in Virginia (NOW HIRING)

PY DATA, INC.

MLOps Engineer

Mclean, VA · On-site

MLOps Engineer Hybrid McLean, VA 12 months Interview Process: - Round 1: 30-minute job-fit interview - Round 2: 1-hour technical coding assessment interview (for candidates who pass job-fit) Must ...

New

Kapitus

MLOps Architect

Arlington, VA · On-site

$117K - $189K/yr

MLOps & GenAI Platform Architecture * Design and implement scalable ML and LLM infrastructure on AWS (SageMaker, EKS, S3, IAM, Lambda, Step Functions, CloudWatch). * Architect end-to-end ML and ...

Biz First

MLOps Engineer

Arlington, VA · On-site

MLOps Engineer BizFirst is assisting our client with the hiring of an MLOps Engineer to build and operate the infrastructure, tooling, and processes that keep machine learning models running reliably ...

New

Biz First

MLOps Engineer

Arlington, VA · On-site

MLOps Engineer BizFirst is assisting our client with the hiring of an MLOps Engineer to build and operate the infrastructure, tooling, and processes that keep machine learning models running reliably ...

New

Steampunk

MLOps Engineer

Mclean, VA · On-site

$115K - $150K/yr

We are seeking a MLOps Engineer to design, build, and support the infrastructure, tooling, and automation that enable scalable and reliable machine learning systems across our client engagements.

Entarian

MLOps Engineer

Arlington, VA

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI ...

Entarian

MLOps Engineer

Arlington, VA

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI ...

Steampunk

MLOps Engineer

Mclean, VA · On-site

$115K - $150K/yr

Overview We are seeking a MLOps Engineer to design, build, and support the infrastructure, tooling, and automation that enable scalable and reliable machine learning systems across our client ...

Steampunk

MLOps Engineer

Mclean, VA · On-site

$115K - $150K/yr

Overview We are seeking a MLOps Engineer to design, build, and support the infrastructure, tooling, and automation that enable scalable and reliable machine learning systems across our client ...

Entarian

MLOps Engineer

Arlington, VA · On-site

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines, focusing on automating model deployment, monitoring model health, detecting data drift, and managing AI ...

Interon IT Solutions

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer Location: Reston VA - In person interviews so need Local In EAST coast only Description: MLOps Platform Engineer The Data Modeling Analytics & AI Engineering team is seeking ...

Tech Tammina LLC

MLOps Platform Engineer

Reston, VA · On-site

MLOps Platform Engineer Location: Reston VA Required Qualifications · 3+ years of hands-on experience with AWS services, including EKS, EC2, S3, IAM, CloudWatch, and ECR. · Strong experience ...

Capital One

Lead Software Engineer - MLOps

Mclean, VA · On-site

Lead Software Engineer - MLOps Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative ...

Socket.dev

Lead Software Engineer - MLOps

Mclean, VA · On-site

$197 - $225/hr

Lead Software Engineer - MLOps Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative ...

New

Capital One

Lead Software Engineer - MLOps

Mclean, VA · On-site

Lead Software Engineer - MLOps Do you love building and pioneering in the technology space? Do you enjoy solving complex business problems in a fast-paced, collaborative, inclusive, and iterative ...

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Showing results 1-20

All JobsMlops JobsMlops Jobs in Virginia

Mlops information

See Virginia salary details

$98.7K

$154.9K

$184.3K

How much do mlops jobs pay per year?

As of Aug 30, 2026, the average yearly pay for mlops in Virginia is $154,911.00, according to ZipRecruiter salary data. Most workers in this role earn between $146,365.00 and $168,247.00 per year, depending on experience, location, and employer.

What is MLOps?

MLOps, short for Machine Learning Operations, is a set of practices that combines machine learning, DevOps, and data engineering to automate and streamline the deployment, monitoring, and maintenance of machine learning models in production. MLOps aims to improve collaboration between data scientists and operations teams, ensuring that models are robust, scalable, and easily updated. It covers the entire machine learning lifecycle, from data preparation to model training, deployment, and ongoing monitoring. By implementing MLOps, organizations can accelerate the development and deployment of reliable machine learning solutions.

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

To thrive as an MLOps Engineer, you need a strong background in machine learning, software engineering, and DevOps principles, often supported by a degree in computer science or a related field. Proficiency with tools like Docker, Kubernetes, CI/CD pipelines, cloud platforms (e.g., AWS, Azure, GCP), and ML frameworks is typically required, along with certifications in cloud or DevOps technologies. Strong problem-solving skills, collaboration, and communication abilities help MLOps professionals excel in cross-functional teams and manage complex workflows. These skills are vital for reliably deploying, monitoring, and scaling machine learning models in production environments, ensuring efficiency and robustness.

What are some common challenges faced by MLOps professionals when deploying machine learning models to production?

MLOps professionals often encounter challenges such as ensuring reproducibility of models, managing version control for both code and data, and maintaining model performance over time. Handling continuous integration and deployment (CI/CD) pipelines for ML models can be complex, especially when dealing with large datasets and evolving algorithms. Additionally, coordinating with data scientists, software engineers, and DevOps teams to streamline workflows and monitor models post-deployment are key responsibilities that require both technical expertise and strong collaboration skills.

What is the difference between Mlops vs Data Engineer?

AspectMlopsData Engineer
Primary FocusDeploying, managing, and monitoring machine learning models in productionBuilding and maintaining data pipelines and infrastructure for data processing
Skills & CertificationsMachine learning, DevOps, cloud platforms, scriptingSQL, ETL, data warehousing, programming
Work EnvironmentCollaborates with data scientists, software engineers, and DevOps teamsWorks with data analysts, data scientists, and software developers
Industry UsageAI/ML projects, production environments, cloud servicesData infrastructure, analytics, big data processing

While both Mlops and Data Engineers work closely with data and cloud technologies, Mlops specialists focus on deploying and maintaining machine learning models in production, ensuring their scalability and reliability. Data Engineers primarily build data pipelines and infrastructure to support data analysis and ML workflows. Understanding these distinctions helps organizations assign the right roles for their AI and data projects.

Is MLOps in demand?

MLOps is a rapidly growing field as organizations increasingly adopt machine learning models in production. Professionals with skills in cloud platforms, automation, and tools like Kubernetes and Docker are highly sought after, reflecting strong industry demand for MLOps expertise.

Is MLOps outdated?

MLOps is an evolving field focused on deploying and managing machine learning models efficiently. It remains highly relevant as organizations increasingly adopt AI solutions, with skills in automation, cloud platforms, and monitoring tools in demand. Staying current with new tools and best practices is essential for MLOps professionals.

What is the average salary in MLOps?

The average salary for MLOps engineers typically ranges from $100,000 to $150,000 annually, depending on experience, location, and company size. Professionals with skills in cloud platforms, automation, and machine learning deployment tend to earn higher salaries.

What are the most commonly searched types of Mlops jobs in Virginia?

The most popular types of Mlops jobs in Virginia are:

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

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

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

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

What cities in Virginia are hiring for Mlops jobs?

Cities in Virginia with the most Mlops job openings:

Mlops jobs near you
Infographic showing various Mlops job openings in Virginia as of August 2026, with employment types broken down into 93% Full Time, 4% Part Time, and 3% Contract. Highlights an 82% Physical, 5% Hybrid, and 13% Remote job distribution, with an average salary of $154,911 per year, or $74.5 per hour.

MLOps Engineer

PY DATA, INC.

Mclean, VA • On-site

Other

Posted 3 days ago

New

Job description

MLOps Engineer

Hybrid McLean, VA

12 months

Interview Process:

- Round 1: 30-minute job-fit interview

- Round 2: 1-hour technical coding assessment interview (for candidates who pass job-fit)

Must haves:

  • Python
  • MLOps
  • Kubernetes
  • Spark
  • AWS

Job Description:

Data Machine Learning Engineer

Must haves:

- Python

- AWS

- Kubernetes

- Kubeflow (or equivalent workflow experience)

- Spark pandas, NumPy

- ML Ops / ML tooling experience

- Hybrid on-site requirement (must be able to work in-office; McLean preferred, New York possible)

Nice to haves:

- SQL / data analysis experience

- Databricks

- Additional ML tooling experience (mlplot, Data bricks)

- DevOps familiarity (Jenkins, CICD

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