Role Overview:

Accountable for the end-to-end architecture, engineering blueprint, deployment model, operational readiness, security, governance and integration strategy of client's enterprise AI systems - ensuring that AI solutions operate as secure, scalable, compliant and business-aligned systems across models, applications, infrastructure, data, networks and external dependencies.

Key Responsibilities:

- Define the overall architecture for enterprise AI systems across models, agents, applications, data, APIs, infrastructure, and external services.

- Establish architecture principles, reference architectures, technology standards, design patterns, and coding standards for AI solutions.

- Ensure AI architectures support scalability, resilience, performance, security, maintainability, and operational sustainability.

- Define production-readiness checklists, standards, and guidelines for AI products.

- Define standardized and reusable AI platform capabilities including foundation models, AI gateways, model routing, vector databases, prompt management, RAG, guardrails, observability, and AI security.

- Own the architectural integration of AI systems with enterprise applications, core banking systems, APIs, identity platforms, data platforms, external AI/model providers, third-party services, and enterprise networks.

- Ensure AI architectures incorporate enterprise security, privacy, risk, and regulatory requirements.

- Define operational architecture and production-readiness requirements for enterprise AI systems.

- Evaluate AI technologies, models, platforms, frameworks, and vendors.

- Review and approve AI solution architectures and technical designs.

Required Experience:

- 20+ years of overall experience in Enterprise Architecture, Solution Architecture, Technology Architecture, or related areas.

- Strong experience architecting and implementing enterprise-scale AI/ML and Generative AI platforms and solutions.

- Experience across cloud, hybrid infrastructure, data, networking, security, integration, and DevSecOps environments.

- Experience in a senior technology leadership role with the ability to engage with business, engineering, security, risk, and executive stakeholders.

Core Skills:

- Enterprise Architecture

- AI/ML Architecture

- AI Governance

- Generative AI / LLMs / AI Agents

- Cloud & Hybrid Architecture

- Kubernetes & Containers

- APIs & Enterprise Integration

- Data Architecture

- Cybersecurity

- DevSecOps / CI-CD

- Observability / SRE

- AI Platforms & MLOps

- Technology & Vendor Strategy