At Chain IQ, your ideas move fast.
Chain IQ is a global AI-driven Procurement Service Partner, headquartered in Baar, Switzerland, with operations across main centers and 16 offices worldwide. We provide tailored, end-to-end procurement solutions that enable transformation, drive scalability, and deliver substantial reductions in our clients' indirect spend. Our culture is built on innovation, entrepreneurship, ownership, and impact. Here, your voice matters - bold thinking is encouraged, and action follows ambition.
We are rebuilding procurement technology and operations into a modern, AI-driven platform. This role ensures the organization evolves as a coherent, AI-native system, not a collection of disconnected solutions.
You will define how product, platform, data, security, internal systems, and client-facing services fit together, with a strong emphasis on agent-driven execution, knowledge-based intelligence, and cost-aware value add automation. Your focus is on clear system structure, consistent patterns, and long-term scalability, not on owning individual implementations.
This is a senior, hands-on role in the CTO office. You operate across Product, Platform, Data, Security, Compliance, Ops, CIO, and Professional Services, ensuring decisions align with a clear system model and that AI-driven capabilities scale reliably, safely, and economically in production.
Define and maintain the target architecture across product, platform, data, AI, security, internal systems, and client integration layers.
Establish architectural principles, system boundaries, and integration patterns that guide design decisions across all teams.
Ensure system-level coherence by aligning solution architecture, platform capabilities, data structures, AI execution models, and security controls.
Arbitrate cross-domain tradeoffs, making explicit decisions where product, data, AI, security, and operational concerns intersect.
Define patterns for agent-driven systems, including orchestration loops, tool usage, delegation models, and failure handling.
Establish standards for retrieval and context assembly, combining structured data, graph relationships, and vector-based search.
Define approaches for state and memory management across workflows, platforms and agent interactions.
Guide design decisions around model integration, inference cost, runtime configuration, caching, and performance tradeoffs to ensure scalable cost-to-serve.
Define clear boundaries for human vs automated decision-making, including validation, escalation, and explainability patterns, in strong collaboration with UX
Guide the transition from services-heavy delivery to a platform-first, automation-driven operating model.
Define and standardize multi-tenant, client isolation, and integration models across internal and external systems.
Provide architectural guidance for complex initiatives and client scenarios, ensuring alignment with long-term system design.
Develop and maintain reference architectures and reusable patterns across core capabilities.
Review and challenge designs where necessary to maintain alignment with architectural principles, without becoming a delivery bottleneck.
Collaborate with the Chief of Staff to ensure architectural decisions are reflected in ways of working and efficient organizational processes.
Provide architectural guidance for complex initiatives and client scenarios, ensuring alignment with long-term system design.
Develop and maintain reference architectures and reusable patterns across core capabilities.
Review and challenge designs where necessary to maintain alignment with architectural principles, without becoming a delivery bottleneck.
Collaborate with the Chief of Staff to ensure architectural decisions are reflected in ways of working and efficient organizational processes.
End-to-end system architecture spanning:
- Product services, APIs, and workflow engines
- Agent-based execution and orchestration systems
- Retrieval and context pipelines (structured, graph, vector)
- Memory and state management for long-running workflows
- Model integration and inference patterns, including cost/performance tradeoffs
- Identity, access control, and security boundaries
- Internal enterprise systems and external client environments
Event-driven and asynchronous system patterns.
Multi-tenant, client-isolated architectures.
Full-stack including omni-platform frontend and engagement channels
Human–AI interaction models and decision boundaries.
Cloud-native platforms and integration models.
Operating model design linking product, platform, and services.
Target architecture definitions across all major system layers.
Architectural principles and standards guiding cross-team decisions.
Reference architectures and reusable system patterns.
System boundary definitions, service interaction models, and data flow diagrams.
Architecture decision records capturing tradeoffs, constraints, and rationale.
Guidance for scaling patterns, integration models, and extensibility.
Architectural input into major initiatives and complex client scenarios.
Strong experience in enterprise or platform architecture across complex, distributed systems.
Proven ability to design systems that integrate application, data, workflow, and AI-driven execution layers into a coherent whole.
Proven experience in defining and driving Telemetry-based Full-Stack Observability, ensuring telemetry data can be used in real-time to continuously monitor, control and enhance our systems.
Experience with cloud-native architectures, event-driven systems, and API-first design.
Practical understanding of agent-based systems, retrieval patterns, and AI-assisted workflows.
Solid grounding in data architecture concepts, including relational, graph, and retrieval-based systems.
Familiarity with identity, access control, and secure multi-tenant design.
Ability to reason about scalability, system evolution, and cost-to-serve in AI-driven environments.
Experience working across product, CX/UX/UI, engineering, data, security, and business stakeholders.
Strong communication skills with the ability to explain complex architectural decisions clearly and pragmatically.
Comfort operating in an environment where systems, models, and requirements evolve rapidly.
A pragmatic mindset balancing architectural clarity with real-world delivery constraints.
We define principles and boundaries, not bureaucracy.
Architecture enables teams to move faster by providing clarity, not by creating approval gates.
Decisions are made once, documented, and reused.
We prioritize coherence across the system over local optimization.
We design systems to evolve, not to be perfect upfront.
AI is treated as a core execution layer, not an isolated capability.
As the platform scales, complexity increases across product, data, workflows, and integrations. Without clear architectural direction, fragmentation and inefficiency will follow.
This role ensures that the organization builds on a coherent, scalable, AI-native foundation, where agent-driven execution, retrieval-based intelligence, and human oversight work together reliably.
It ensures that our platform scales not just technically, but economically, embedding cost-aware, explainable, and reliable AI behaviour into the system architecture.
Join a truly global team.
We offer a dynamic and international environment where high performance meets real purpose. We're proud to be Great Place to Work-certified and even prouder of the people who make that possible. Let’s shape the future of procurement - together.
Chain IQ – Create. Lead. Make an impact.
Information for agencies: Applications sent or uploaded by placement agencies or similar are not desired, will therefore not be considered and will be deleted.
