AI Foundation Architect, Keyrus

• You are an experienced technical architect who combines strategic architecture judgement with recent, practical understanding of production AI systems
• You move between the complete system and its individual components without trying to own the implementation of every layer
• You stay close enough to GenAI and agentic engineering to understand how these systems behave in production, not only how they look on a diagram
• You are credible with engineers and client architects alike
• You can explain why an architecture decision matters for security, reliability, scalability, performance, cost and long-term operability
• You know when a decision needs depth and when it needs coordination across specialists
• Degree in Computer Science, Engineering or a related field, or equivalent professional experience
• Typically 8+ years 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 how models, data, applications, integrations and infrastructure interact
• Experience working directly with client architects, engineering leaders, security teams and technical decision-makers
• Native or bilingual-level English, written and spoken
• Deep architecture expertise in at least one major cloud and AI ecosystem, with the 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
• Data architecture in scope: lakehouse, data mesh, streaming and feature stores
• Ability to define and communicate reference architectures, technical standards, interface boundaries, non-functional requirements and architecture decision records
• Strong technical judgement and transparent trade-offs across performance, reliability, security, maintainability, latency, cost and operational complexity
• Experience architecting RAG, agentic or multi-model systems, including retrieval, model routing, tool access, state, memory, agent execution and human oversight
• Experience designing model access and deployment patterns across managed AI services, APIs and open-source models
• Understanding of MLOps / LLMOps, CI/CD, containers, infrastructure automation and monitoring from an architecture perspective
• Experience conducting architecture reviews and guiding multiple engineering disciplines within a shared target architecture
• Depth in environments such as Azure AI, AWS Bedrock, GCP Vertex AI, OpenAI, Anthropic, Mistral, Kubernetes, Kafka, Databricks, Snowflake, Apache Iceberg, Palantir Foundry, Terraform, MCP, AI gateways or OpenTelemetry. These are examples, not a checklist

Desirable

• 🕒 Contract type: Employee
• 🗓 Target start date: November 2026
• ⏰ Working hours: Full-time
• At Keyrus, we help organisations transform complexity into measurable outcomes by combining technology, data, intelligence and human decision-making
• We bring together deep technical expertise and business understanding to turn AI experimentation into reliable, scalable operational intelligence
• As an AI Foundation Architect, you will help organisations transform complexity into measurable outcomes by combining technology, data, intelligence, and human decision-making
• This role is both technical and consultative — perfect for someone who can combine architecture leadership, hands-on technical depth, and close engagement with client stakeholders
• We’re looking for professionals who think like architects and act like builders
• You will define the technical architecture that lets production AI and agentic systems operate reliably at enterprise scale: how models, agents, data, applications, APIs, cloud services, security controls and operational capabilities fit together
• You set the principles and boundaries that guide AI Engineers, Software Engineers, Data Engineers and Forward Deployed Engineers, and within your assigned scope you are accountable for the integrity of the target design
• 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 and observability
• Review solution and engineering designs for architectural integrity, approving material design decisions within your assigned scope where you are the designated architecture authority
• Design secure, scalable and operable foundations, ensuring reliability, identity, access, auditability, recovery, maintainability and human-control requirements are considered from the start
• Own architecture-level AI FinOps, including model and provider routing, consumption patterns, capacity decisions and cost optimisation
• Guide specialist engineering teams without replacing their ownership, working with AI, Software and Data Engineers, AI Governance specialists and delivery teams on cross-domain decisions
• Engage with client architecture and technical stakeholders, explaining choices, dependencies, risks, trade-offs and required decisions in clear technical and business terms
• Convert successful decisions into reusable Keyrus assets, including reference architectures, decision records, standards and design guidance
• ⭐ What Makes You Successful
• You make architecture decisions explicit rather than letting them emerge accidentally during implementation
• You balance technical ambition with production reality, challenge unnecessary complexity and choose patterns teams can build, operate and evolve
• You know which decisions require architecture authority and which should stay with specialist engineers
• You use production constraints (security, reliability, latency, cost, scalability, maintainability) to guide design
• You communicate architectural trade-offs clearly to technical teams and client stakeholders
• You create reusable architecture knowledge rather than solving every engagement from zero