Founding AI Lead / AI Systems Architect

M7 is a global House of Method built to help businesses see clearly, decide boldly, and build better.At the core of M7 is the M7 Brain — our proprietary intelligence system connecting Method, evidence, knowledge, decisions and standards to govern how intelligence operates across the organization.Through the M7 Brain and Ayn, our conversational intelligence, we are building an environment where human judgment and AI work together across strategy, decision-making and execution.Based in Dubai and built for global businesses, M7 is developing a new model for how an AI-native strategic organization can operate.Role DescriptionThe Founding AI Lead / AI Systems Architect is a full-time, on-site role based in Dubai, UAE, working directly with M7’s founders.This is not a traditional enterprise architecture role, and it is not a prompt-engineering position.You will be responsible for architecting, building and continuously evolving the intelligence infrastructure behind M7.Your primary responsibility will be the M7 Brain and its surrounding AI ecosystem, including Ayn, AI agents, knowledge and retrieval systems, evaluation infrastructure, governance controls and integrations with M7’s internal and client-facing platforms.You will work across architecture and implementation — from defining how the system should think, retrieve, remember, evaluate and escalate, to building the technical infrastructure that makes those behaviours reliable in production.You will also play a central role in M7’s long-term AI architecture, engineering standards and technical roadmap.What You’ll Work OnArchitecture and evolution of the M7 BrainAyn, M7’s conversational intelligenceAgentic and multi-agent systemsLLM orchestration and model routingRAG, knowledge retrieval and context engineeringMemory, provenance and source-of-truth architectureAI evaluation, simulation, regression testing and observabilityHuman-in-the-loop approval and escalation systemsAI permissions, security and governanceStructured and unstructured data pipelinesAPIs and integrations with external platformsProduction reliability, monitoring and scalabilityInternal tooling for continuous AI improvementTechnical architecture connecting the Brain, M7 Workspace and client experiencesQualificationsWe care more about what you have built than the titles you have held.You should bring:Strong hands-on experience building production LLM and AI systemsDeep understanding of agentic architectures, RAG, context engineering and knowledge systemsStrong software engineering and backend architecture capabilitiesExperience designing scalable APIs, data pipelines and distributed systemsPractical experience with major LLM APIs, frameworks and modern AI infrastructureUnderstanding of AI evaluation, observability, reliability and failure modesExperience with cloud infrastructure, databases, vector retrieval and modern deployment environmentsStrong understanding of security, permissions and data governanceAbility to move between architecture and codeAbility to translate business concepts into rigorous technical systemsStrong product judgment and ability to work directly with foundersExperience in early-stage or zero-to-one technology environments is highly valuedA degree in Computer Science, Engineering or a related discipline is welcome, but exceptional practical experience matters more.What Makes This Role DifferentWe are not looking for someone to add AI features to an existing business.We are building an organization in which AI is part of the operating architecture itself.The questions are bigger:What should the system know?What should it remember?What should it be allowed to decide?How should multiple agents work from the same truth?How do we preserve evidence and provenance?When should AI challenge a human?When must a human take control?How do we know the intelligence is actually improving?If these are the kinds of problems you want to solve, we want to hear from you.Please apply with your profile and examples of AI systems you have personally designed or built. GitHub, technical architecture work, deployed products or detailed case studies are more valuable to us than a conventional cover letter.