Senior AI Solutions Architect Lead

This Department of War enterprise data and analytics program delivers mission-critical capabilities that enable leaders across the Department to make faster, better-informed decisions using trusted data dos Digital Modernization sector is seeking an experienced SME AI Solutions Architect Lead to support the delivery, enhancement, and adoption of enterprise data and analytics products used across multiple DoD organizations.

In this role, you will work alongside government partners, engineers, and other industry teammates to translate operational and strategic requirements into scalable, production-ready solutions. You will contribute directly to product planning, execution, and continuous improvement – helping ensure capabilities are delivered efficiently, aligned to mission priorities, and positioned for sustained success.

This position offers the opportunity to work on a high-visibility, enterprise program at the intersection of data, analytics, and emerging AI technologies. Ideal candidates are motivated by mission impact, comfortable operating in complex stakeholder environments, and interested in building deep domain expertise while delivering capabilities with real-world national security outcomes.

• Design and implement end-to-end AI architectures that meet mission, performance, and security requirements.
• Define system topology, data flows, model serving patterns, and integration points using scalable frameworks.
• Select and configure AI platforms and model orchestration tools to ensure high availability and low latency.
• Collaborate with software, data, and platform engineers to validate architecture decisions and optimize deployment patterns.
• Ensure compliant, production-grade implementation of AI capabilities across classified and unclassified environments.
• Lead a team of 8‑15 direct reports, providing mentorship and guidance to enhance team performance.
• Develop and maintain a System Engineering Plan (SEP) to manage all systems architecture aspects.
• Conduct systems engineering activities to specify, build, and maintain system engineering designs.
• Manage requirements and maintain a system requirements management environment.
• Support enterprise system architecture activities to define and scope the AI solutions.
• Define, document, and maintain APIs and technical standards for interoperability.
• Engineer and continuously improve the underlying infrastructure of the AI platform.
• Identify and integrate government, commercial, and open-source tools into the AI environment.
• Design and enhance user interface (UI) and user experience (UX) components of the platform.
• Implement and maintain services for production-ready AI/ML models.
• Ensure cybersecurity compliance and maintain cybersecurity architecture for the AI system.
• Perform site reliability engineering to maintain a reliable and efficient AI platform.

• Active Top Secret (TS) clearance with SCI eligibility.
• Bachelor’s degree in Computer Science, Artificial Intelligence, Data Science, Engineering, or related technical discipline and 8‑12 years of relevant experience OR Master’s degree in a related field and 6‑10 years of relevant experience.
• Minimum of 10 years of experience in systems engineering and AI and/or data intelligence architectures.
• Experience architecting and deploying enterprise AI/ML solutions in cloud environments (AWS, Azure, or GCP).
• Experience designing and delivering AI/ML solutions in enterprise cloud environments (AWS, Azure, or GCP).
• Experience integrating AI/ML capabilities into production systems using APIs and microservices architectures.
• Experience developing AI/ML pipelines including data preparation, model training, validation, and deployment.
• Experience working across cross-functional teams to deliver integrated technical solutions.
• Experience operating within SAFe or large‑scale Agile frameworks supporting enterprise systems.
• Experience with system architecture design and implementation in classified environments.
• Experience developing Agentic AI solutions such as autonomous planning‑execution‑reflection loops, multi‑agent collaboration and coordination, and tool usage patterns including API…