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Deployment Engineer Jobs in Virginia (NOW HIRING)

Entarian

MLOps Engineer

Arlington, VA · On-site

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Model Deployment : Deploy and manage machine learning models in production using tools like MLflow ...

Entarian

MLOps Engineer

Arlington, VA · On-site

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Model Deployment : Deploy and manage machine learning models in production using tools like MLflow ...

Entarian

MLOps Engineer

Arlington, VA · On-site

The MLOps Engineer will design, implement, and maintain end-to-end machine learning pipelines ... Model Deployment : Deploy and manage machine learning models in production using tools like MLflow ...

The Consortium, Inc.

Openshift Clusters Deployment

VA · On-site

Red Hat OpenShift Engineer skilled in container orchestration, platform management, and cloud ... Automate cluster provisioning, configuration and deployment pipelines using tools like Ansible ...

VTG

Systems Engineer Expert

Herndon, VA · On-site

$150K - $205K/yr

We are seeking a highly motivated Azure Pipeline Deployment Engineer to support enterprise scale cloud modernization and secure software delivery initiatives. In this role, you will design, implement ...

VTG

Systems Engineer Expert

Herndon, VA · On-site

$150K - $205K/yr

We are seeking a highly motivated Azure Pipeline Deployment Engineer to support enterprise scale cloud modernization and secure software delivery initiatives. In this role, you will design, implement ...

Metron

DevSecOps Engineer

Reston, VA · On-site

The DevSecOps Engineer will work across Azure DevOps Server, Nexus, SonarQube, Kubernetes/K3s deployment workflows, artifact controls, and secure release patterns to help teams deliver software ...

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All JobsDeployment Engineer JobsDeployment Engineer Jobs in Virginia

Deployment Engineer information

See Virginia salary details

$35.2K

$108.6K

$168.5K

How much do deployment engineer jobs pay per year?

As of Aug 12, 2026, the average yearly pay for deployment engineer in Virginia is $108,621.00, according to ZipRecruiter salary data. Most workers in this role earn between $79,800.00 and $137,300.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a deployment engineer?

To thrive as a Deployment Engineer, you need a solid background in software development, systems administration, and deployment methodologies, often supported by a degree in computer science or related field. Familiarity with configuration management tools (like Ansible, Puppet, or Chef), CI/CD pipelines, and cloud platforms such as AWS or Azure is typically required. Problem-solving, attention to detail, and strong communication skills distinguish top performers in this role. These skills are crucial for ensuring smooth, reliable software releases and effective collaboration with cross-functional teams.

What is the difference between Deployment Engineer vs Network Engineer?

AspectDeployment EngineerNetwork Engineer
Required CredentialsBachelor's in CS or IT, certifications like Cisco CCNA, CompTIA Network+Bachelor's in CS, IT, or related field; Cisco CCNA, CompTIA Network+ often preferred
Work EnvironmentData centers, client sites, cloud environmentsCorporate offices, data centers, network operation centers
Industry UsageIT services, cloud providers, telecomTelecommunications, enterprise IT, service providers
Common Search/ComparisonDeployment Engineer vs Network Engineer

Deployment Engineers focus on implementing and configuring software or hardware solutions across various environments, ensuring smooth deployment processes. Network Engineers specialize in designing, maintaining, and troubleshooting network infrastructure. While both roles require networking knowledge and certifications, Deployment Engineers often work closely with software and system deployment, whereas Network Engineers focus on network connectivity and security.

What is a deployment engineer?

A deployment engineer is a computer system specialist who installs and maintains networks, software, or computer systems. As a deployment engineer, your responsibilities include troubleshooting issues related to routers and wireless networks, training customers how to use methods or implement upgrades, and ensuring that security is functioning properly on all network assets. Qualifications to become a deployment engineer include proficiency with networks protocols, proprietary programs, and equipment. Many employers prefer candidates with customer support experience.

What are some common challenges faced by deployment engineers during software rollout, and how are they typically addressed?

Deployment Engineers often face challenges such as coordinating with multiple teams, managing unexpected technical issues during rollout, and ensuring minimal downtime. These are typically addressed by thorough planning, automated deployment pipelines, and clear communication with stakeholders. Proactive testing in staging environments and having rollback strategies in place also help mitigate risks and ensure smooth deployments.
More about Deployment Engineer jobs
What are the most commonly searched types of Deployment Engineer jobs in Virginia? The most popular types of Deployment Engineer jobs in Virginia are:
What are popular job titles related to Deployment Engineer jobs in Virginia? For Deployment Engineer jobs in Virginia, the most frequently searched job titles are:
What job categories do people searching Deployment Engineer jobs in Virginia look for? The top searched job categories for Deployment Engineer jobs in Virginia are:
What are popular job titles related to Deployment Engineer jobs in VA? For Deployment Engineer jobs in VA, the most frequently searched job titles are:
Deployment Engineer jobs near you
Infographic showing various Deployment Engineer job openings in Virginia as of August 2026, with employment types broken down into 91% Full Time, 4% Part Time, and 5% Contract. Highlights an 86% Physical, 5% Hybrid, and 9% Remote job distribution, with an average salary of $108,621 per year, or $52.2 per hour.

MLOps Engineer

Entarian

Arlington, VA • On-site

Full-time

Re-posted 16 days ago

Job description

Overview/ Job Responsibilities
Job Summary
We are seeking a skilled MLOps Engineer to join our team and ensure the seamless deployment, monitoring, and optimization of AI models in production.
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-related logging. This role will involve building scalable infrastructure and dashboards for real-time and historical insights, ensuring models are secure, performant, and aligned with business needs.
Key Responsibilities
  • Model Deployment: Deploy and manage machine learning models in production using tools like MLflow, Kubeflow, or AWS SageMaker, ensuring scalability and low latency.
  • Monitoring and Observability: Build and maintain dashboards using Grafana, Prometheus, or Kibana to track real-time model health (e.g., accuracy, latency) and historical trends.
  • Data Drift Detection: Implement drift detection pipelines using tools like Evidently AI or Alibi Detect to identify shifts in data distributions and trigger alerts or retraining.
  • Logging and Tracing: Set up centralized logging with ELK Stack or OpenTelemetry to capture AI inference events, errors, and audit trails for debugging and compliance.
  • Pipeline Automation: Develop CI/CD pipelines with GitHub Actions or Jenkins to automate model updates, testing, and deployment.
  • Security and Compliance: Apply secure-by-design principles to protect data pipelines and models, using encryption, access controls, and compliance with regulations like GDPR or NIST AI RMF.
  • Collaboration: Work with data scientists, AI Integration Engineers, and DevOps teams to align model performance with business requirements and infrastructure capabilities.
  • Optimization: Optimize models for production (e.g., via quantization or pruning) and ensure efficient resource usage on cloud platforms like AWS, Azure, or Google Cloud.
  • Documentation: Maintain clear documentation of pipelines, dashboards, and monitoring processes for cross-team transparency.

Minimum Qualifications
Qualifications
  • Education: Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related field.
  • Experience:
    • 5+ years in MLOps, DevOps, or software engineering with a focus on AI/ML systems.
    • Proven experience deploying models in production using MLflow, Kubeflow, or cloud platforms (AWS SageMaker, Azure ML).
    • Hands-on experience with observability tools like Prometheus, Grafana, or Datadog for real-time monitoring.
  • Technical Skills:
    • Proficiency in Python and SQL; familiarity with JavaScript or Go is a plus.
    • Expertise in containerization (Docker, Kubernetes) and CI/CD tools (GitHub Actions, Jenkins).
    • Knowledge of time-series databases (e.g., InfluxDB, TimescaleDB) and logging frameworks (e.g., ELK Stack, OpenTelemetry).
    • Experience with drift detection tools (e.g., Evidently AI, Alibi Detect) and visualization libraries (e.g., Plotly, Seaborn).
  • AI-Specific Skills:
    • Understanding of model performance metrics (e.g., precision, recall, AUC) and drift detection methods (e.g., KS test, PSI).
    • Familiarity with AI vulnerabilities (e.g., data poisoning, adversarial attacks) and mitigation tools like Adversarial Robustness Toolbox (ART).
  • Soft Skills:
    • Strong problem-solving and debugging skills for resolving pipeline and monitoring issues.
    • Excellent collaboration and communication skills to work with cross-functional teams.
    • Attention to detail for ensuring accurate and secure dashboard reporting.
  • Must be eligible to obtain a Department of Homeland Security EOD clearance ( Requirements 1. US Citizenship, 2. Favorable Background Investigation)

Desired Qualifications
Preferred Qualifications
  • Experience with LLM monitoring tools like LangSmith or Helicone for generative AI applications.
  • Knowledge of compliance frameworks (e.g., GDPR, HIPAA) for secure data handling.
  • Contributions to open-source MLOps projects or familiarity with X platform discussions on #MLOps or #AIOps.

About Us
Formed through the strategic union of Sev1Tech and ERT, Entarian is a premier provider of mission-critical engineering and technology solutions. Founded on a legacy of excellence dating back to 1993, Entarian is a product of an evolved and fully diversified engineering and federal technology leader. From deep space to defense and civilian missions, Entarian delivers secure, mission-aligned digital solutions that drive national resilience and operational effectiveness. We don't just support modernization; we define it.
Join the Mission and Start your Career Journey: Apply Directly via our Careers Portal Connect, Referrals & Inquiries? Email the team: careers@entarian.com
Entarian is an Equal Opportunity and Affirmative Action Employer. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, pregnancy, sexual orientation, gender identity, national origin, age, protected veteran status, or disability status.

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