Cyber Systems Engineer​/AI Governance Lead​/Solutions Architect

LMI is seeking a Cyber Systems Engineer/AI Governance Lead/Solutions Architect to provide full-time senior technical leadership for Department of Veterans Affairs (VA) modernization initiatives involving enterprise architecture, artificial intelligence, data, automation, cloud, and other emerging technologies. This role combines independent solution-architecture judgment with practical AI and technology-governance leadership so complex solutions are technically feasible, secure, supportable, responsible, and aligned with VA enterprise constraints.

This position follows a hybrid work model, with an expectation of approximately 25% onsite presence at LMI's Tysons headquarters or Washington, DC.

The successful candidate will evaluate solution alternatives, integration patterns, data and identity dependencies, supportability, lifecycle risk, and enterprise fit while translating VA and federal governance expectations into usable design criteria, controls, documentation, and escalation paths. The role should connect architecture and governance early enough to influence decisions rather than reviewing them only after a design is substantially complete.

This position requires a senior practitioner who can move between executive-level tradeoffs and detailed technical questions, challenge assumptions, identify material AI or technology risk, right-size governance to the use case, and guide teams toward reusable patterns without becoming the default day-to-day architect or developer.

The ideal candidate combines enterprise and solution architecture depth with strong technology-risk judgment, responsible-AI fluency, and the communication skills to explain complex technical and governance issues to engineers, cybersecurity/privacy specialists, data teams, clinicians, program leaders, and executives.

• Lead independent solution-architecture and AI/technology-governance review for complex VA modernization initiatives.
• Evaluate solution options for enterprise fit, reuse, integration, maintainability, supportability, scalability, security, data dependencies, and lifecycle cost.
• Establish risk-tiering and review criteria so governance depth is proportionate to intended use, affected users, data sensitivity, autonomy, operational impact, and potential harm.
• Translate VA and federal policy, responsible-AI expectations, cybersecurity/privacy requirements, and enterprise standards into practical architecture requirements, controls, and decision criteria.
• Review higher-risk AI, data, clinical, identity, automation, and workflow use cases andidentifywhenadditionalvalidation, formal escalation, or executive decision is required.
• Shape major technical tradeoffs across applications, APIs/integration, data, cloud/platform, identity, low-code, automation, and AI-enabled solution patterns.
• Define non-functional requirements covering interoperability, data protection, identity, logging/auditability, observability, resilience, accessibility, human oversight, monitoring, and operational support.
• Maintain architecture decision records, governance assessments, assumptions, approvals, exceptions, risk treatments, accountable owners, and unresolved questions for traceability.
• Develop reusable reference architectures, governance checklists, technical patterns, review templates, and decision guidance that accelerate future VA initiatives.
• Facilitate architecture and governance reviews that produce clear decisions, owners, actions, and escalation paths rather than unresolved technical debate.
• Partner with cybersecurity/privacy, data, clinical informatics, HCD, platform, engineering, testing, and program leadership to resolve cross-cutting constraints and right-size controls.
• Assess technical debt, vendor lock-in, sustainment, operational ownership, model/system change, and post-deployment monitoring before major decisions are finalized.
• Support implementation and readiness reviews to confirm material architecture and governance assumptionsremainvalid as solutions move from design into pilot, deployment, or scale.
• Track recurring architecture and governance findings and recommend shared services, reference patterns, standards, or portfolio-level improvements that reduce one-off solution design.

• Bachelor's degree in computer science, information systems, engineering, cybersecurity, data science, public policy, risk management, or a related field; equivalent professional experience may be considered.
• 10+ years of progressive experience across…