AI Platform Architect

• Consolidated experience in technical architecture roles, typically from 5 years onwards in its specialization domain.

• Demonstrated and deep experience in at least one of the corporate ecosystem environments: on-prem (Kubernetes on-prem, VMware) or cloud (Azure, AWS, GCP), with an understanding of the hybrid model.

• Design and implementation of self-service AI platforms: model access mediation (AI gateway), credentials, routing, quotas, and cost control.

• Agentic systems architecture: orchestration, context management, tool integration (MCP, function calling), and RAG patterns.

• Harness engineering: design of the agent execution environment (context, tools, isolation, permissions, and verification) so that its output is repeatable and reviewable.

• Model governance: lifecycle of approved models, selection criteria, quality evaluation, and traceability of use.

• AI-specific security: prompt injection, data leakage, and agent execution isolation.

• Observability and cost control over the use of models and agents.

• Integration of AI into the SSDLC: CI/CD pipelines, IaC, and agent execution in Kubernetes.

Join our team as a 💻AI Platform Architect 💻 for a major client in the banking sector. On-site presence 2 days per week in central Madrid (Alcalá) is essential.

Design and evolve the corporate AI platform as a product that enables teams to build, deploy, and operate AI capabilities and agentic systems autonomously, securely, and traceably, across the entity's hybrid ecosystem (multicloud, on-prem, and Mainframe). The scope covers the model access and mediation layer, the governance of its use and cost, the integration of AI into the development lifecycle through agentic execution environments (harness), and the design of the controls that make such use auditable and compliant with the regulatory framework. The role encompasses both the design and the construction of the platform and accompanies the product throughout its entire lifecycle.

• Architecture design and documentation.

• Ability to analyze and resolve complex technical problems.

• Direct involvement in the construction phase: technical decision-making in real contexts and responsibility for the consistency between design and what is built.

• Clear technical communication with all technology teams.

• Understanding of the banking regulatory environment as it applies to its domain.

• Orientation toward the internal product: treating the platform as a product with real users.

• Ability to design on top of immature technology, avoiding coupling to specific providers and models.

• Judgment to delimit what is automated with AI and what requires human control, based on risk.