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# AI/ML Engineer

- **Company:** 540
- **Location:** Arlington, VA, United States
- **Experience:** Experienced
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Airflow, Amazon Web Services, Application Frameworks, Automation of Tests, Microsoft Azure, Cloud Computing, Continuous Integration, Distributed Computing Environment, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Software Engineering, Management of Software Versions, Google Cloud, Feature Engineering, Retrieval-Augmented Generation, Delivery Pipeline, Large Language Models, Model Validation, Generative AI, Containerization, Kubernetes, Infrastructure Automation Frameworks, Information Technology, Performance Monitor, Data Management, Machine Learning Operations, Api Design, Software Version Control, Data Pipelines, Docker, Microservices
- **Published:** July 30, 2026
- **Apply:** https://www.clearancejobs.com/jobs/9066800/aiml-engineer

## About the Role

Citizenship & Clearance Requirement: Per client requirements, candidates must be U.S. Citizens with an active DoW Secret (or higher) clearance
Education Requirement: Bachelor's degree in Computer Science, Engineering, or a related technical field preferred; equivalent combinations of education and relevant experience will be considered
540 Internal Thrive Level: Software Engineer II or III, 4+ years of relevant AI/ML engineering, software engineering, or data science experience
Experience developing and deploying production-grade AI or machine learning systems
Proficiency with Python and commonly used AI/ML frameworks
Experience building automated model training, validation, deployment, and monitoring pipelines
Experience with MLOps platforms, practices, and tools
Experience deploying models in cloud-based or containerized environments
Experience developing APIs, microservices, or model-serving capabilities for batch or real-time inference
Understanding of model evaluation, performance monitoring, drift detection, explainability, and governance
Experience with Docker, Kubernetes, or similar containerization and orchestration technologies
Experience with CI/CD, infrastructure as code, automated testing, and source control
Experience working within AWS, Azure, or Google Cloud
Familiarity with data pipelines, feature engineering, distributed data processing, and data versioning
Ability to troubleshoot issues across applications, infrastructure, data, and machine learning systems
Strong communication and collaboration skills, including the ability to document and explain technical decisions

NICE TO HAVE

Experience supporting DoW, federal, Advana, or other enterprise AI/ML and data platforms
Experience with AWS SageMaker or comparable cloud AI/ML platforms
Experience with MLflow, Kubeflow, Airflow, Argo Workflows, Ray, Feast, or similar tools
Experience building AI/ML solutions in secure, regulated, classified, or mission-critical environments
Familiarity with large language models, generative AI, retrieval-augmented generation, or foundation-model operations
Experience implementing responsible AI, model-risk-management, or AI-governance practices
Currently holds, or is willing to obtain within 30 days of employment, an approved certification such as CCSP, CFR, FITSP-M, GSEC, Security+, or SSCP

## Description

540 is seeking an AI/ML Engineer to support a mission-critical technology modernization effort for the Department of War. You will design, build, and maintain production AI/ML services and infrastructure that enable teams to develop, deploy, monitor, and scale models supporting complex defense missions.

Working with software engineers, data engineers, data scientists, cybersecurity teams, and mission stakeholders, you will build reusable ML capabilities and automated pipelines using modern software engineering and MLOps practices. The ideal candidate enjoys solving complex engineering challenges and building secure, reliable AI/ML systems that directly support mission outcomes., Design, build, and maintain AI/ML services, products, and lifecycle capabilities supporting WDP
Develop automated pipelines for model training, validation, testing, deployment, and monitoring
Create reusable frameworks, libraries, and shared components that accelerate AI/ML development
Build model-serving capabilities supporting secure, scalable, and reliable batch or real-time inference
Implement MLOps practices using CI/CD, infrastructure as code, automated testing, and source control
Develop model monitoring, performance tracking, drift detection, and operational health capabilities
Support model explainability, reproducibility, governance, and lifecycle traceability
Manage model versions, artifacts, datasets, and feature-engineering workflows
Optimize AI/ML services and infrastructure for performance, scalability, reliability, and cost efficiency
Collaborate with data engineers and data scientists to prepare data and operationalize models
Partner with cybersecurity teams to implement security, access-control, auditing, and governance requirements
Troubleshoot issues spanning models, applications, data pipelines, infrastructure, and production services
Document AI/ML architectures, engineering processes, and operational procedures


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