Senior AI/ML Engineer
540
| Company | 540 |
| Category | Engineering |
| Location | Arlington |
| Remote | On-site (inferred) |
| Employment | Not stated |
| Level | Senior |
| Salary | Not stated by the employer |
| Posted | 29 Jul 2026 |
| Last verified | 2 Aug 2026 |
| Source | Employer ATS (greenhouse) |
Description
540 is seeking a Senior AI/ML Engineer to support a mission-critical technology modernization effort for the Department of War. You will lead the design and evolution of 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 translate complex requirements into secure, scalable AI/ML solutions. You will define MLOps standards, guide technical delivery, and establish reusable capabilities supporting the end-to-end machine learning lifecycle.
Location : Arlington, VA 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: Senior Software Engineer
WHY 540?
540 is a forward-thinking company that the government turns to in order to #getshitdone. We don’t just talk about innovation – we deliver it. We break down barriers, build impactful technology, and solve mission-critical problems.
HOW YOU’LL DRIVE IMPACT
Lead the architecture and evolution of AI/ML services, platforms, and lifecycle capabilities supporting WDP
Translate mission requirements into scalable AI/ML architectures and implementation strategies
Define MLOps standards, reusable patterns, and best practices across engineering teams
Architect automated pipelines for model training, validation, testing, deployment, and monitoring
Develop reusable frameworks, libraries, and shared components that accelerate AI/ML delivery
Design model-serving platforms supporting secure, scalable, and reliable batch or real-time inference
Establish model monitoring, performance tracking, drift detection, explainability, and governance capabilities
Define practices for model versioning, artifact management, reproducibility, feature engineering, and data lineage
Optimize AI/ML services and infrastructure for performance, scalability, reliability, and cost efficiency
Establish CI/CD, infrastructure-as-code, automated testing, and operational practices for AI/ML systems
Lead technical reviews and resolve complex issues spanning models, applications, data, infrastructure, and production services
Partner with cybersecurity teams to incorporate security, access control, auditing, and governance requirements
Communicate architecture decisions and mentor engineers and data scientists on AI/ML engineering and MLOps practices
REQUIRED SKILLS & EXPERIENCE
9+ years of relevant AI/ML engineering, software engineering, or data science experience
Experience leading the design and delivery of enterprise-scale, production-grade AI/ML systems
Advanced software engineering experience using Python and commonly used AI/ML frameworks
Experience architecting automated model training, validation, deployment, and monitoring pipelines
Experience defining MLOps architecture, standards, and practices across engineering teams
Experience designing model-serving capabilities for batch and real-time inference
Experience deploying and operating models in cloud-based or containerized environments
Strong understanding of model evaluation, monitoring, drift detection, explainability, reproducibility, and governance
Experience with Docker, Kubernetes, or similar containerization and orchestration technologies
Experience establishing CI/CD, infrastructure-as-code, automated testing, and source-control practices
Experience architecting AI/ML solutions within AWS, Azure, or Google Cloud
Experience with data pipelines, distributed data processing, feature engineering, and data versioning
Ability to evaluate technical approa
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