AI/ML Systems Engineer

Responsibilities

• Lead the application of AI/ML, large language models, agentic workflows, and data analytics to ground systems architecture and systems engineering challenges.
• Develop AI-enabled approaches for analyzing large engineering datasets, including requirements, architecture artifacts, interface data, test results, operational data, defect trends, and technical documentation.
• Use agentic AI methods to support architecture trade studies, design decision analysis, risk identification, technical baseline assessment, modernization planning, and mission/thread analysis.
• Identify opportunities to improve CI/CD and DevSecOps pipelines through AI/ML-assisted automation, anomaly detection, test prioritization, quality gates, deployment insights, documentation support, and engineering workflow optimization.
• Lead the development and documentation of the Government Reference Architecture (GRA) for ground segments, ensuring alignment with Air Force strategic goals and objectives.
• Analyze existing and emerging ground segment architectures, technologies, and standards to inform the GRA development process.
• Support ground systems architecture development, interface analysis, system decomposition, requirements traceability, technical reviews, and integration planning.
• Translate user needs and future platform requirements into the GRA, ensuring alignment with interoperability objectives.
• Develop and deliver comprehensive technical documentation for the GRA, including architectural diagrams, interface specifications, and implementation guidelines.
• Define architectural principles, standards, and guidelines to promote interoperability, modularity, severability, and scalability across future adopting platform ground segments.
• Partner with engineering and software teams to design repeatable, secure, and auditable AI/ML workflows suitable for controlled, or mission-critical environments.
• Define human-in-the-loop review processes, validation methods, governance controls, and traceability mechanisms for AI-assisted engineering recommendations.
• Evaluate emerging AI/ML, agentic AI, data engineering, and Machine Learning Operations (MLOps) technologies for applicability to ground systems and digital engineering environments.
• Communicate technical findings, architecture recommendations, AI/ML opportunities, and implementation roadmaps to program leadership and government customers.
• Help establish reusable AI/ML-enabled systems engineering practices, patterns, and reference architectures across programs.

Qualifications

• Required Security Clearance: Active Top Secret clearance with eligibility for Sensitive Compartmented Information (SCI).
• Bachelor's degree in Systems Engineering, Software Engineering, Computer Science, Data Science, Aerospace Engineering, or a related technical discipline.
• Minimum of 20 years of experience in systems engineering, with a focus on ground systems architecture and standards.
• Experience developing or working with architectural reference models or frameworks is highly desired.
• Experience applying MBSE methodologies in DoD environments is preferred, especially in the context of architecture modeling.
• Proficiency in MBSE tools such as Cameo Systems Modeler, MagicDraw, or Enterprise Architect is highly desirable.
• Experience supporting ground systems, mission systems, command and control systems, defense systems, or other complex technical architectures.
• Strong understanding of systems engineering principles, architecture development, requirements analysis, interface definition, integration, verification, and technical decision-making.
• Working knowledge of AI/ML concepts, data analytics, large language models, agentic workflows, retrieval-augmented generation, or applied automation.
• Ability to translate architecture and engineering problems into data-driven or AI/ML-enabled solution approaches.
• Ability to work across systems engineering, software, cybersecurity, cloud/platform, test, and program management teams.
• Desired Familiarity with MLOps, model evaluation, prompt engineering, AI governance, AI assurance, or secure deployment of AI-enabled capabilities.
• Experience with GitLab, Jenkins, Kubernetes, containers, cloud environments, artifact repositories, automated test frameworks, or pipeline observability tools.