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 workplace
- 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.

📌 Artificial Intelligence Architect (Bengaluru)
🏢 Viti Infosystem India
📍 Bengaluru