AI Architect

CONTEXT

Our client, a major player in its sector, is moving from AI experimentation to responsible industrial deployment. Generative AI, machine learning, and agentic AI are no longer side projects; they are becoming capabilities that must fit into a broader architecture, a security model, and a public-service duty of care. The organization needs an architect who can define the target landscape before delivery teams scatter in different directions. This mission is about creating the reference architectures, integration patterns, and guardrails that let AI systems be useful without becoming unmanageable. The architect will work in a hybrid setup in Brussels, connecting business ambitions, delivery realities, privacy requirements, security constraints, and operational observability. The story here is one of careful enablement: giving teams a path to build, while ensuring that identity, logging, model context, retrieval, and lifecycle management are designed as part of the platform rather than patched on later.

ACTIVITIES

• Define and maintain target architectures, reference architectures, and integration patterns for AI across the organization.
• Align AI architecture with broader enterprise architecture, security, privacy, continuity, and manageability requirements.
• Develop reference designs for generative AI, machine learning, and agentic AI across on-premises, cloud, and hybrid deployments.
• Define architectural frameworks for AI gateways, model context, retrieval, agents, identity, logging, observability, and lifecycle management.
• Guide delivery teams through architecture reviews, design decisions, and reusable platform components.
• Integrate privacy and security by design into AI solutions and identify risks before systems reach production.
• Identify reusable platform components that reduce duplication and accelerate safe deployment.
• Collaborate with security, privacy, infrastructure, data, and business stakeholders to keep architecture practical and aligned.

EXPECTED ACHIEVEMENTS

• A coherent AI target architecture exists and is understood by delivery teams across the organization.
• Generative AI, machine learning, and agentic AI projects follow shared reference patterns instead of isolated designs.
• Security, privacy, continuity, and observability are embedded into AI solutions from the start.
• Reusable platform components reduce delivery friction and improve consistency across teams.
• Architecture reviews become a constructive mechanism for guiding safe, responsible AI deployment.
• The organization can deploy AI for public services and internal operations with confidence, traceability, and control.

ENVIRONMENT

• Enterprise architecture, solution architecture, and architecture review practices.
• Generative AI, machine learning, and agentic AI platforms and patterns.
• Hybrid, cloud, and on-premises deployment models.
• AI gateways, retrieval architectures, model context management, agents, and identity integration.
• Logging, observability, lifecycle management, privacy, and security by design.
• Collaboration with delivery, security, privacy, infrastructure, and business teams in a hybrid Brussels setup.