Senior AI/ML Engineer

Five Forty•Arlington, TX

1d•Onsite

About The Position

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.

Requirements

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 approaches and clearly communicate architecture decisions, risks, and tradeoffs
Experience leading technical reviews, mentoring engineers, and influencing technical direction
Ability to troubleshoot complex issues across applications, infrastructure, data, and machine learning systems

Nice To Haves

Experience leading AI/ML initiatives within DoW, federal, Advana, or other enterprise data environments
Experience architecting solutions using AWS SageMaker or comparable cloud AI/ML platforms
Experience with MLflow, Kubeflow, Airflow, Argo Workflows, Ray, Feast, or similar technologies
Experience building AI/ML platforms in secure, regulated, classified, or mission-critical environments
Experience with large language models, generative AI, retrieval-augmented generation, or foundation-model operations
Experience establishing responsible AI, model-risk-management, or AI-governance practices
Experience leading AI/ML platform modernization, technology evaluations, or proofs of concept
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

Responsibilities

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