Accountability and activities

1. Enterprise AI Architecture Strategy & Governance

• Define and own enterprise AI/GenAI architecture blueprint and standards
• Establish target-state architecture aligned with business and data strategy
• Act as ultimate architecture authority across all AI initiatives

2. AI & GenAI Platform Ownership (Enterprise Level)

• Own strategy and design of enterprise AI platforms, shared services, and reusable components

• Ensure platforms support scalability, reusability, and cross-business adoption
• Drive platform investment priorities and roadmap

3. Technical Leadership for AI Use Cases (Across BU)

• Provide technical direction and architecture governance across all AI use cases
• Ensure solutions are production-grade, scalable, and aligned with enterprise architecture
• Resolve complex architectural challenges and trade-offs

4. End-to-End Architecture Quality & Performance Accountability

• Accountable for architecture quality, system performance, and reliability at scale
• Define standards for monitoring, optimization, and performance tuning
• Ensure time-to-value through efficient and reusable design

5. Responsible AI, Security & Risk Architecture

• Define and enforce Responsible AI, security, and compliance architecture frameworks
• Ensure integration with enterprise risk, data governance, and cybersecurity standards
• Act as technical authority for AI risk mitigation

6. Technology Strategy & Ecosystem Leadership

• Own evaluation and selection of AI technologies, tools, and platforms
• Define build vs buy vs partner strategy
• Lead strategic alignment with vendors, partners, and internal engineering teams

7. Standards, Reusability & Engineering Excellence

• Establish enterprise-wide architecture patterns, design standards, and reusable assets
• Drive consistency and efficiency across AI solutions
• Promote engineering best practices and technical excellence

Qualification

• IT, Data Science, AI – MBA is a plus

Experience

• Experience with MLOps/DevOps (CI/CD pipelines, monitoring, model deployment)
• Experience working in complex enterprise environments with data privacy, security, and regulatory requirements
• Hands-on experience with LLMs (OpenAI, Azure OpenAI, Claude, Llama, or equivalents)

Certification

Skill and Knowledge

• Strong background in AI architecture, ML engineering, GenAI system design, or Applied AI
• Deep understanding of RAG, vector stores, embeddings, prompt engineering, guardrails
• Excellent communicator capable or bridging technical teams and business sponsors