Senior Agentic AI Architect

XMS Solutions is an IT and cybersecurity company supporting federal agencies and commercial organizations. We specialize in identity and access management, cloud technologies, cybersecurity, and IT modernization. Our team is committed to delivering innovative solutions, solving complex challenges, and supporting mission-critical operations.

XMS Solutions offers an excellent benefits package, including 100% employer-paid medical, dental, and vision premiums for employees. Additional benefits include paid time off, paid holidays, a 401(k) with company match, and company-paid life and disability insurance.

Position Overview

XMS Solutions is seeking an experienced Agentic AI Architect to lead the architecture and technical direction of enterprise AI solutions. This is a senior, hands-on strategic role for an architect who can assess complex mission and business needs, make sound technology decisions, and guide solutions from initial concept through secure, reliable production deployment.

The ideal candidate brings a strong foundation in enterprise solution architecture along with demonstrated experience designing generative AI and agent-based systems. You will partner with technical leadership, engineering teams, and stakeholders to develop practical AI capabilities that integrate with existing systems and deliver measurable value.

Key Responsibilities

• Lead the end-to-end architecture of enterprise agentic AI solutions, including autonomous and human-in-the-loop workflows, AI assistants, bots, and digital avatars where appropriate.
• Translate complex organizational requirements into scalable solution designs, technical roadmaps, architecture decisions, and implementation standards.
• Assess and select LLMs, orchestration frameworks, retrieval patterns, tools, and infrastructure based on mission fit, performance, cost, and risk, and maintainability.
• Design secure integrations with enterprise applications, APIs, identity systems, data sources, and knowledge repositories.
• Architect and oversee the integration and deployment of AI-powered chatbots and virtual assistants within Microsoft Teams and other enterprise applications, ensuring secure, scalable, and reliable operation.
• Direct proof-of-concept efforts, establish success criteria, and determine readiness for production through testing, evaluation, and measurable outcomes.
• Provide technical leadership and architecture reviews for engineering teams; mentor developers and influence stakeholders without relying solely on formal authority.
• Define approaches for observability, evaluation, guardrails, access controls, resiliency, and responsible AI governance across the solution lifecycle.
• Communicate architectural tradeoffs, risks, dependencies, and recommendations clearly to both technical and nontechnical decisionmakers.

Skills & Experience

Required Qualifications

• Extensive professional experience in enterprise architecture, solution architecture, software engineering, or related technical disciplines, including senior-level design ownership.
• Demonstrated experience architecting AI/ML or generative AI solutions, with working knowledge of LLM capabilities, limitations, evaluation, and production integration patterns.
• Proven ability to lead the design of complex, integrated solutions across APIs, cloud or on-premises environments, enterprise data, and security boundaries.
• Hands-on experience integrating and deploying chatbots within Microsoft Teams and other enterprise applications, including production implementation and ongoing support.
• Experience taking emerging technology initiatives beyond prototypes into supported, secure, maintainable solutions.
• Strong technical judgment, documentation skills, and the ability to explain tradeoffs and influence senior stakeholders.
• U.S. citizenship and an active Top Secret security clearance.

Preferred Qualifications

• Direct architecture experience with agentic AI systems, tool use, multi-agent orchestration, or human-in-the-loop automation.
• Experience with Retrieval-Augmented Generation (RAG), embeddings, vector databases, and enterprise knowledge retrieval.
• Experience implementing LLM evaluation, monitoring, model security, safety controls, and AI governance practices.
• Familiarity with cloud AI platforms and modern deployment approaches, including MLOps, DevSecOps, and infrastructure automation.
• Experience delivering solutions in federal, regulated, or other security-sensitive environments.