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Mlops Engineer Jobs in Virginia (NOW HIRING)

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 ...

Kapitus

MLOps Architect

Arlington, VA · On-site

$117K - $189K/yr

MLOps & GenAI Platform Architecture * Design and implement scalable ML and LLM infrastructure on ... Data ingestion & preprocessing o Feature engineering / embedding generation o Model training & fine ...

Adtech

AI/ML Engineer With DevOps

Ashburn, VA · On-site

$54 - $74/hr

Adtech seeks a motivated, career and customer-oriented AI/ML Engineer . This is currently a hybrid ... Expertise with MLOps tools and frameworks such as Mlflow, Kubeflow, Airflow and implementing ...

AIToolboard

AI Engineer (Remote)

Fort Belvoir, VA · On-site

$150 - $200/hr

Experience collaborating with MLOps, DevOps, and infrastructure engineers to ensure seamless model deployment and infrastructure scaling Experience working with AWS, Azure, or GCP cloud platforms and ...

Phase2 Technology

Applied AI Engineer

Fort Belvoir, VA · On-site

$125 - $150/hr

You will be part of a large community of AI engineers across the company and collaborate with data engineers, data scientists, solutions architects, and MLOps engineers to deliver world‑class ...

Noblis

AI/ML Engineer, Senior

Chantilly, VA · On-site

$107K - $146K/yr

Develop and maintain DevOps and MLOps pipelines to streamline model development, testing, deployment, and monitoring. * Deploy and support AI/ML systems within secure, classified, and high side ...

Interon IT Solutions

AI/ML Engineer

Reston, VA · On-site

AI/ML Engineer (Python, AWS, GenAI) Location: Reston, VA (In-person interviews required) Candidate ... Build and support MLOps workflows: model versioning, containerized training/inference, automated ...

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Mlops Engineer information

See Virginia salary details

$97.7K

$153.4K

$178K

How much do mlops engineer jobs pay per year?

As of Sep 8, 2026, the average yearly pay for mlops engineer in Virginia is $153,394.00, according to ZipRecruiter salary data. Most workers in this role earn between $147,338.00 and $165,330.00 per year, depending on experience, location, and employer.

What is an MLOps engineer?

An MLOps Engineer is responsible for deploying, monitoring, and maintaining machine learning models in production. They bridge the gap between data science and operations by automating workflows, optimizing infrastructure, and ensuring model reliability. Their role includes CI/CD for ML models, data pipeline management, and performance monitoring. They also work with cloud platforms, containerization, and orchestration tools to scale ML systems efficiently.

What are some common challenges MLOps engineers face in their daily work?

Mlops Engineers often encounter challenges in integrating new machine learning models into existing production systems while ensuring minimal downtime and maintaining data integrity. Managing the scaling and orchestration of models across various cloud or on-prem environments can be complex, requiring close coordination with data scientists and DevOps teams. Staying up to date with rapidly evolving tools and best practices is also essential in this field. Addressing these challenges provides valuable opportunities to innovate and improve both technical processes and team collaboration.

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

To thrive as an Mlops Engineer, you need strong skills in software engineering, machine learning pipelines, and cloud infrastructure, often backed by a degree in computer science, engineering, or a related field. Familiarity with tools such as Docker, Kubernetes, TensorFlow, AWS/GCP/Azure, and CI/CD systems is essential, and certifications like AWS Certified Machine Learning or Kubernetes Administrator are often valued. Effective communication, problem-solving, and teamwork are crucial soft skills for collaborating across data science and IT teams. These abilities enable Mlops Engineers to efficiently deploy, manage, and scale machine learning models in dynamic production environments.

What do you need to be a MLOps engineer?

To become a MLOps engineer, you typically need a strong background in software engineering, machine learning, and cloud platforms. Proficiency in programming languages like Python, experience with containerization tools such as Docker, and knowledge of CI/CD pipelines are essential. Certifications in cloud services and familiarity with tools like Kubernetes and ML frameworks also enhance qualifications.

Who earns more, ML engineer or MLOps engineer?

MLOps engineers typically earn slightly more than ML engineers due to their focus on deploying, maintaining, and scaling machine learning systems, which requires expertise in cloud platforms, automation, and infrastructure. Salary differences can vary based on experience, location, and company size, but MLOps roles often command higher compensation because of their specialized skill set.

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

The most popular types of Mlops Engineer jobs in Virginia are:

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

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

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

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

What cities in Virginia are hiring for Mlops Engineer jobs?

Cities in Virginia with the most Mlops Engineer job openings:

Mlops Engineer jobs near you
Infographic showing various Mlops Engineer job openings in Virginia as of August 2026, with employment types broken down into 91% Full Time, 5% Part Time, and 4% Contract. Highlights an 87% Physical, 4% Hybrid, and 9% Remote job distribution, with an average salary of $153,394 per year, or $73.7 per hour.

MLOps Platform Engineer

Interon IT Solutions

Reston, VA • On-site

Contractor

Re-posted 8 days ago

Job description

#W2 Role
 
Job title: 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 an experienced MLOps Platform Engineer to design, build, and support enterprise-grade machine learning operations capabilities. This role will play a key part in enabling scalable, reliable, and secure ML model development and deployment across our cloud and container platforms.
This is a hands-on engineering role requiring strong expertise in AWS, Kubernetes (EKS), CI/CD automation, containerization, and ML platform operations. The ideal candidate will have solid engineering fundamentals combined with practical knowledge of ML workflows, deployment patterns, and platform reliability.
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Key Responsibilities
Platform Engineering & Operations
 
· Engineer, manage, and support MLOps platform components across AWS and EKS-based environments.
· Oversee deployment, configuration, and operation of infrastructure used for ML training, batch inference, and real-time model serving.
· Ensure platform availability, resilience, and performance across dev, test, and production environments.
· Implement role-based access controls (RBAC), network policies, and scalable namespace designs within EKS.
 
Model Deployment & CI/CD Automation
· Build and support CI/CD pipelines (GitLab) for model packaging, container image builds, vulnerability scanning, and automated deployment flows.
· Enable standardized model release processes including environment promotion, versioning, and rollback workflows.
· Integrate CI/CD with ML frameworks, model repositories, artifacts, and runtime environments.
 
Container & Kubernetes Workloads
· Design and manage EKS workloads supporting containerized ML jobs and microservices.
· Implement auto-scaling, resource quotas, cluster optimization, and multi-tenant workload isolation.
· Support GPU and CPU-based training/inference workloads.
 
Monitoring, Observability & Optimization
· Implement logging, monitoring, and alerting for ML pipelines, model endpoints, batch jobs, and platform components.
· Analyze compute, storage, and data transfer usage to optimize cost efficiency across ML workloads.
· Perform incident response, root cause analysis, and long-term remediation planning.
 
Collaboration & Enablement
· Partner with Data Scientists, ML Engineers, and application teams to operationalize end-to-end machine learning solutions.
· Provide technical guidance on best practices for ML model lifecycle management, deployment patterns, and scalable architectures.
· Contribute to documentation, runbooks, onboarding materials, and internal knowledge bases.
 
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Required Qualifications
· 3+ years of hands-on experience with AWS services, including EKS, EC2, S3, IAM, CloudWatch, and ECR.
· Strong experience operating and troubleshooting Kubernetes (preferably AWS EKS).
· Proficiency in containerization (Docker) and orchestration concepts.
· Strong programming/scripting experience in Python and Bash.
· Experience building and managing CI/CD pipelines (GitLab or equivalent).
· Familiarity with machine learning workflows, including training, inference, and model monitoring.
· Experience with infrastructure-as-code (Terraform or CloudFormation).
· Experience supporting production platforms, including incident management and root cause analysis.
 
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Preferred Qualifications
· Experience managing Data Analytics Platforms / Tools (e.g., Domino, SageMaker)
· Experience with ML lifecycle tools such as MLflow, or similar.
· Experience supporting GPU-based workloads or distributed training environments.
· Familiarity with enterprise MLOps architectures and patterns (batch, real-time, microservices).
· Understanding of data processing frameworks and feature pipelines.
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Other Competencies
· Strong analytical, troubleshooting, and problem-solving skills.
· Effective communication and documentation abilities.
· Ability to collaborate across engineering, analytics, and product teams.
· Self-motivated with the ability to drive initiatives independently.
· Ability to work in a complex, regulated enterprise environment.