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Lead AI Systems Architect (Full-Stack) at MPF Federal. Lead AI Systems Architect (Full-Stack) . MPF Federal is seeking a hands-on, Lead AI Systems Architect to design and build sophisticated AI applications from the ground up. This role is for a builder who thrives in ambiguity, takes ownership of architectural decisions, and is eager to deliver high-impact products at speed within government and heavily regulated environments. . COMPENSATION: $150k-$180k (Depends on verified experience). In this role you will own the end-to-end development of AI-enabled applications that bridge the gaps between complex data ingestion, machine learning models, and advanced front-end visualizations.  . What You’ll Do . Architect & Build from Scratch: Lead the design and development of AI-driven tools optimized for deployment across AWS, Azure, and Google Cloud. . Execute in Multi-Cloud Governance Structures: Build products that ensure operational discipline and robust governance across diverse cloud ecosystems. . Create Advanced Full-Stack Orchestration: Develop end-to-end applications where the frontend serves as a real-time Decision Support Interface and the backend functions as a high-concurrency engine. . Exploit Real-Time Data & API Architecture: Design asynchronous APIs capable of handling live, multi-modal data ingestion from diverse sources. . Employ Stateful System Design: Architect complex system states where variables are collected, analyzed, and orchestrated in real-time to drive predictive outcomes. . Deliver Cloud-Agnostic Deployment: Ensure seamless application performance and portability across a multi-cloud landscape. . Required Qualifications . 10+ years in full-stack development, with at least 4 years specifically focused on AI/ML integration. . Proven success building and deploying applications in secure, multi-tenant cloud environments. . Experience designing unified data stacks that synchronize real-world human interactions with virtual system states in real-time. . A track record of developing autonomous AI agents using frameworks such as LangGraph, AutoGen, or CrewAI to orchestrate complex, multi-step workflows. . Experience building pipelines for diverse data types (text, telemetry, and unstructured audio) to drive real-time system updates. . Proficiency in designing agent-based models and utilizing Reinforcement Learning to simulate complex decision-making cycles. . Deep LLM & Vector expertise, leveraging LLMs for dynamic content generation and utilizing vector databases to manage contextual memory. . Demonstrated ability to operate as a self-starter, managing technical debt and architectural trade-offs independently. . Experience navigating the technical and security constraints of government or heavily regulated industries. . U.S. Citizenship Required. . Company Location: United States.