AI Platform Architect

• Consolidated experience in technical architecture, typically 5 years or more within your area of specialization.

• Deep experience in, at least, one of these environments:

  • Cloud: Azure, AWS or GCP.

  • On-premise: Kubernetes on-premise or VMware.

• Good understanding of hybrid architectures and the integration between different technological environments.

• Experience designing or implementing self-service AI platforms and mediation mechanisms for model access.

• Solid knowledge of agentic system architectures, orchestration, context management, MCP, function calling, and RAG.

• Experience or advanced knowledge in harness engineering and the design of secure and controlled execution environments for agents.

• Knowledge of AI model lifecycle governance, evaluation, selection, and traceability.

• Experience applying AI-specific security principles.

• Knowledge of observability and mechanisms to control and optimize the cost of models and agents.

• Experience with CI/CD, IaC, and Kubernetes within the development lifecycle.

• Ability to design and document architectures and resolve technical problems of high complexity.

• Ability to work with technologies that are still evolving, designing solutions that reduce coupling to specific providers or models.

• Judgment to determine which processes can be automated via AI and which must maintain supervision or human intervention mechanisms based on risk.

• Ability to communicate technical decisions and concepts clearly to different technology teams.

• Understanding of the banking regulatory environment in those aspects related to AI architecture and utilization.

• Product orientation, understanding the platform as an internal product that must respond to the needs of real users and teams.

We are looking for a professional with solid experience in technological architecture to participate in the design, construction, and evolution of a corporate Artificial Intelligence platform. The objective will be to enable technology teams to build, deploy, and operate AI capabilities and agentic systems autonomously, securely, scalably, and traceably within a hybrid ecosystem.

You will have direct involvement in both the definition of the architecture and its construction and evolution, accompanying the platform throughout its entire lifecycle and ensuring consistency between the design and its actual implementation.

• Design and evolve the corporate AI platform architecture across hybrid environments: multicloud, on-premise, and Mainframe.

• Build self-service capabilities to facilitate the consumption of AI models by different technology teams.

• Define the access and mediation layer for models via AI Gateway, including credential management, routing, quotas, and cost control.

• Design architectures for agentic systems, considering orchestration, context management, tool integration via MCP and function calling, and RAG patterns.

• Design the execution environment or harness for agents, establishing context, tools, isolation, permissions, and verification mechanisms that allow for repeatable and reviewable results.

• Define the governance of the model lifecycle, including selection criteria, approved models, quality evaluation, and usage traceability.

• Incorporate specific security controls for AI, especially against risks such as prompt injection, information leakage, or inadequate access during agent execution.

• Define observability, traceability, and cost control mechanisms associated with the use of models and agents.

• Integrate AI capabilities within the secure software development lifecycle, including CI/CD, infrastructure as code, and execution on Kubernetes.

• Design the necessary controls to guarantee an auditable use of AI aligned with the requirements of the banking regulatory environment.

• Actively participate in the construction of the platform, making technical decisions and ensuring that the implementation maintains consistency with the defined architecture.

• Document architecture decisions and components, facilitating their understanding and adoption by different technology teams.