Senior AI/ML 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.

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