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. Develop solutions and recommendations to improve data exchange, communication protocols, and functional integration. 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. Experience with DevSecOps, CI/CD pipelines, software delivery workflows, or modern software engineering environments. 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. Modern Technology Solutions, Inc. (MTSI) is a 100% employee-owned engineering services and solutions company that provides high-demand technical expertise in Digital Transformation, Modeling and Simulation, Rapid Capability Development, Test and Evaluation, Artificial Intelligence, Autonomy, Cybersecurity and Mission Assurance. MTSI delivers capabilities to solve problems of global importance. Founded in 1993, MTSI today has employees at over 20 offices and field sites worldwide. For more information about MTSI, please visit MTSI embraces nine core values including our first core value of Employees come first. Consistent with our Core Values, we are committed to Equal Opportunity, making decisions without regard to race, color, religion, sex, national origin, age, military/veteran status, disability, or any other characteristics protected by applicable law. MTSI is committed to Equal Employment Opportunity and providing reasonable accommodations to applicants and employees with physical and/or mental disabilities. #J-18808-Ljbffr Modern Technology Solutions, Inc.