Artificial Intelligence Architect

Key responsibilities

1. End-to-end AI system architecture

• Define the overall architecture systems powered by AI across models, Microservices, agentic framework, infrastructure, external services & Integration.
• Establish architecture principles, reference architectures and technology standards.
• Define the technology selection principles and coding standards
• Define architecture standards to be followed for enterprise applications powered by AI
• Ensure architecture supports scalability, resilience, Performance and maintainability

2 . AI engineering ecosystem & Architecture

• Define/Architect standardized reusable capabilities along the following dimensions
• Foundation models/Proprietary models
• AI gateways
• Model routing
• Vector databases
• Prompt management
• RAG
• Guardrails
• Observability
• AI security

3. Business rules & AI controls

• Ensure business rules, policies, decision logic and human-in-the-loop controls are properly incorporated into Enterprise systems
• Define boundaries between LLM/model behaviour and deterministic business logic (i.e what goes to LLM vs. What is not going to LLM)
• Define or follow enterprise Architecture patters for enterprise systems, AI systems and hybrid systems in a manner its outputs can be controlled, validated and audited.

4. Integration & external ecosystem

• Own the architectural integration of enterprise system powered by AI with the following:
• Core banking / enterprise applications
• APIs
• Identity platforms
• Data platforms
• External AI/model providers
• Third-party services
• Enterprise networks
• Assess dependencies and architectural risks associated with external providers.

5. Security, risk & compliance

• Ensure AI architecture incorporates security and regulatory requirements.
• Define controls for data privacy, Application security & AI application security (like Model security, model abuse, data leakage.
• Work with Cybersecurity, Risk, Legal and Compliance teams to establish application controls including AI related controls.
• Ensure appropriate auditability and traceability is defined and implemented

6. Technology & vendor strategy

• Evaluate/aware of the latest trends of enterprise trend of latest technology including AI.
• Define/follow technology selection criteria and enterprise standard
• Design standards for opensource tool stack selection
• Approve opensource tools before they are deployed in Enterprise environment
• Establish technology lifecycle and obsolescence strategy.

7. Architecture governance

• Review and approve Enterprise solution architectures including AI platform and applications.
• Establish architecture review checkpoints.
• Maintain enterprise AI reference architecture and standards.
• Identify and manage technical debt and architectural risks.