AI Foundation Architect

Why Keyrus, Why Now! Keyrus is an international group of 2,800 consultants and experts across 28 countries , built on a single conviction: AI does not transform businesses. Architected intelligence does. For more than 30 years, we have been building the data foundations that make intelligent systems work — designing the Operating System of the intelligent enterprise, where intelligence is embedded into the core of business processes to create sustainable value: we operationalise intelligence. AI does not replace humans. It repositions us to a place no system can follow: understanding, deciding, designing, and creating. At Keyrus, you will not just develop skills - you will develop judgment. Your expertise sharpens with every system you architect, every client challenge you solve, every deployment that compounds on the last. Over time, you grow into one of the rarest professionals of the intelligence era: someone who bridges data, AI, and human decision-making at scale, across industries and geographies. This is not a role you fill. It is a discipline you master and a story you help write to become a Keyrus Architect of Intelligence. Technology amplifies. Keyrus culture differentiates . Industrial discipline connects the two. What You'll Architect As an AI Foundation Architect, you will define the technical architecture that enables production AI and agentic systems to operate reliably at enterprise scale. You will define how models, agents, data, applications, APIs, cloud services, security controls, and operational capabilities fit together, and set the architecture principles and boundaries that guide AI Engineers, Software Engineers, Data Engineers, and other technical teams. Within your assigned architecture scope, you are accountable for the integrity of the target design. Depending on the engagement, this may include formal design approval; explicitly define broader enterprise governance authority rather than assume it. Your primary focus is client-facing architecture and technical design. You will also turn successful approaches into reusable Keyrus architecture patterns and standards without automatically owning an internal platform product or roadmap. Your responsibilities - Define target architectures for production GenAI and agentic systems, connecting models, agents, enterprise data, applications, APIs, cloud infrastructure, security, and operational services. - Establish architecture principles, reference patterns, technical boundaries, and non-functional requirements that engineering and delivery teams can apply consistently. - Make and document material architecture decisions across platform selection, model access, integration patterns, agent execution, data access, deployment, scalability, resilience, observability, and architecture-level cost. - Review solution and engineering designs for architectural integrity, approving material design decisions within the assigned scope where the role has been designated as the architecture authority. - Design secure, scalable, and operable foundations for intelligent systems, ensuring that reliability, identity, access, auditability, recovery, maintainability, and human-control requirements are considered from the start. - Guide specialist engineering teams without replacing their ownership, working with AI Engineers, Software Engineers, Data Engineers, Governance specialists, and delivery teams on decisions that cross technical domains. - Engage with client architecture and technical stakeholders, explaining architecture choices, dependencies, risks, trade-offs, and required decisions in clear technical and business terms. - Convert successful architecture decisions into reusable Keyrus assets, including reference architectures, decision records, standards, patterns, and design guidance that can strengthen future engagements. - Architecture-level AI FinOps, including model/provider routing, consumption patterns, capacity decisions, and cost optimisation. Who You Are You are an experienced technical architect who combines strategic architecture judgment with recent practical understanding of production AI systems. You can move between the complete system and its individual components without trying to become the implementation owner for every layer, and you know when an architecture decision requires depth and when it requires coordination across specialists. You remain close enough to modern GenAI and agentic engineering to understand how these systems actually behave in production – not only how they appear on an architecture diagram. You are credible with engineers and client architects alike and can explain why an architecture decision matters for security, reliability, scalability, performance, cost, and long-term operability. What You Bring Must Have - Seniority threshold -8+ years designing complex architectures, set locally - Strong experience designing and owning architecture for complex production technology environments, with clear accountability for cross-system technical decisions. - Recent practical experience architecting production GenAI, LLM, or agentic systems, including the interaction between models, data, applications, integrations, and operational infrastructure. - Deep architecture expertise in at least one major cloud and AI ecosystem, with sufficient breadth to evaluate comparable technologies and integration approaches. - Strong understanding of distributed systems, APIs, integration architecture, identity and access, security, scalability, resilience, observability, and production operations. - Ability to define and communicate reference architectures, technical standards, interface boundaries, non-functional requirements, and architecture decisions. - Strong technical judgment and the ability to make transparent trade-offs across performance, reliability, security, maintainability, latency, cost, and operational complexity. - Data architecture in scope - Lakehouse, data mes