Senior AI System Architect (Outcome-Based AI

Dynatech International LLC•Palm Bay, FL

5d•Onsite

About The Position

We're actively deploying AI-driven operational systems to modernize sourcing, procurement, proposal development, workflow automation, executive operations, and enterprise decision support. This is not a traditional "AI chatbot" position. We're building production-grade AI operational infrastructure designed to create measurable business outcomes, improve operational velocity, enhance executive decision-making, and support scalable autonomous workflows across the enterprise. We're looking for an AI Systems Architect to help design, implement, and mature the next generation of AI-enabled operational systems at Dynatech. The Senior AI Systems Architect will lead the design and implementation of enterprise-grade AI operational systems integrating large language models (LLMs), autonomous agents, workflow orchestration, retrieval systems, Microsoft ecosystems, operational data sources, and human-in-the-loop governance. This role takes more than API integration and prompt engineering. The architect needs to design durable AI systems that produce measurable operational outcomes while maintaining security, auditability, governance, scalability, and operational resilience. The right candidate is equally at home discussing AI orchestration frameworks, workflow engineering, executive operations, systems integration, and long-term production architecture.

Requirements

U.S. Person as defined in 22 CFR § 120.62 (U.S. citizen, lawful permanent resident, or protected individual)
Familiarity with CMMC Level 2, NIST SP 800-171, and DFARS 252.204-7012 requirements, and with handling Controlled Unclassified Information (CUI)
Comfortable operating in an ITAR-aware environment
Deep experience with OpenAI APIs and LLM architectures
Strong understanding of AI orchestration frameworks
Experience with one or more of: Microsoft Foundry, LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, Microsoft Copilot Studio, or similar frameworks
Experience with vector databases and retrieval systems
Strong API integration experience
Strong Python development capabilities
Experience with Azure AI and Microsoft Graph
Experience with workflow orchestration platforms
Experience integrating modern AI services with legacy enterprise applications
Strong SQL Server background (T-SQL, performance diagnostics, schema discovery)
Comfort with ASP.NET Web Forms and ERP-class applications as integration targets
Comfort working across Windows Server, macOS AI compute, and Tailscale-managed networks
Track record designing durable operational systems
Experience implementing production AI systems
Strong understanding of workflow engineering
Experience with logging, monitoring, and observability
Strong understanding of security and governance
Experience with scalable enterprise architecture
Ability to align AI systems with measurable business outcomes
Strong operational reasoning skills
Ability to translate executive objectives into technical architectures
Strong written and verbal communication skills
Ability to operate independently in a fast-moving environment

Nice To Haves

Experience supporting defense or regulated industries
Experience with procurement or supply chain systems
Experience with autonomous operational workflows
Experience with voice AI systems
Experience with avatar or conversational AI systems
Experience integrating AI into ERP or CRM environments
Experience with executive assistant AI systems

Responsibilities

Architect scalable AI operational systems supporting enterprise workflows
Design modular, maintainable AI infrastructure
Build autonomous and semi-autonomous AI agent ecosystems
Develop long-term memory and retrieval architectures
Implement human-in-the-loop escalation frameworks
Design secure production-grade AI pipelines
Automate operational and executive workflows
Integrate AI into procurement, sourcing, proposal generation, and operational processes
Develop measurable KPI-driven automation systems
Create AI-assisted decision support systems
Design orchestration layers across multiple AI services and tools
Integrate with Microsoft 365, Azure AI, Microsoft Graph, Teams, Outlook, and SharePoint
Support AI-driven email, scheduling, task management, and operational workflows
Implement secure permission and identity management practices
Implement multi-agent orchestration frameworks
Develop RAG (Retrieval-Augmented Generation) systems
Build vector database architectures
Implement tool-use and function-calling frameworks
Develop monitoring, logging, and audit systems
Design AI safety and governance controls
Develop production-grade testing methodologies
Implement AI guardrails and escalation logic
Ensure operational transparency and auditability
Support data governance and compliance requirements
Build systems resilient to hallucination and workflow failure