AI Solution Architect - Mission-Critical Defense Platforms
Systems Planning & Analysis
Overview
Systems Planning and Analysis, Inc. (SPA) delivers high-impact, technical solutions to complex national security issues. With over 50 years of business expertise and consistent growth,we are known for continuous innovation for our government customers, in both the US and abroad. Our exceptionally talented team is highly collaborative in spirit and practice, producing Results that Matter . Come work with the best! We offer opportunity, unique challenges, and clear-sighted commitment to the mission. SPA: Objective. Responsive. Trusted.
The Sea, Land, Air Analysis Group’s mission is to deliver objective analyses, plans and strategies to US Navy, US Marine Corps, USAF, other DoD, and Australian Defence Force clients to support technology development, acquisition, delivery and sustainment decisions. The group provides the core of SPA thought leadership on Undersea Strategy, Software Development and Cost Estimating, with additional benchmark capabilities in AI/ML & Decision Analytics, Modeling and Simulation, and Future Force Assessments.
SPA has a need for an AI Solution Architect, who will serve as the lead designer and integrator of AI and machine‑learning services within the Mission Planning Enterprise platform. This role will focus on applying AI/ML to improve integration productivity, code and system assurance, and analytics for engineering and IVV&E teams. The AI Solution Architect will be responsible for identifying high‑value AI use cases and governing the secure, explainable deployment of AI services on the DevSecOps/CDE stack. .#FC #DICE
ResponsibilitiesThe AI Solution Architect makes AI and machine‑learning services available on the platform to improve integration productivity, code and system assurance, and analytical insight for the OEMs and integration teams. They work with engineering, IVV&E, and platform operations to identify high‑value use cases such as static and dynamic code analysis, automated test generation and prioritization, anomaly detection in performance metrics, and intelligent defect triage. The AI Solution Architect designs, integrates, and governs AI services on the DevSecOps/CDE stack with appropriate security, explainability, and auditability. They also help shape the roadmap for AI‑enabled capabilities that directly support mission planning enterprise objectives, ensuring that AI investments are tightly coupled to SOW‑driven integration, evaluation, and cybersecurity needs.
QualificationsRequired Qualifications:
- Bachelor’s degree in computer science, data science, or a related field
- 7+ years of experience architecting and delivering AI/ML‑enabled solutions, including at least 3 years in complex enterprise or regulated environments
- Strong understanding of software engineering and IVV&E workflows and how AI/ML can improve productivity and quality (e.g., code analysis, test selection, anomaly detection)
- Experience architecting AI services on modern platforms (containers, microservices, APIs) and integrating them with DevSecOps pipelines and tools
- Understanding of security, governance, and assurance considerations for AI, including data protection, model lifecycle management, and traceability of AI outputs
- An active Secret clearance or the ability to obtain one, and the ability to maintain it throughout employment
Desired Qualifications:
- Experience applying AI/ML in defense, aerospace, or other mission‑critical software contexts (e.g., code assurance, cyber analytics, integration verification)
- Familiarity with mission planning software ecosystems and data structures, and an understanding of AI opportunities in integration, performance assessment, and deficiency analysis
- Background integrating AI‑driven analytics into program and technical performance dashboards to provide early indicators of risk
- Experience working closely with cybersecurity and ISSM teams to ensure AI solutions meet RMF and other cyber policy requirements
- Advanced degree in AI, machine learning, or a closely related discipline, and experience with MLOps practices in operational environments
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