AI

AI EA.

• Statement of Work: 

  1. When organizations are looking for an experienced AI Enterprise Architect to augment their capacity to deliver key enterprise architecture deliverables in support of an IT AI program. 

  2. This role will, under the leadership of a foundation enterprise architect, help define key enterprise architecture deliverables to help the IT AI program and support IT teams in building sustainable, secure, and interoperable AI solutions. 

  3. Deliverables this role will contribute to, but not be limited to, are documenting the current state of enterprise architecture related to AI capabilities (which would include different viewpoints such as capability mappings, AI technologies, data flows, reference architectures, design patterns, etc.), researching and analyzing foundation-specific AI use cases, helping shape a future-state AI enterprise architecture, and defining specific AI agent management capabilities needs, guidelines, and standards.

• Skills and experience: (20)

  1. Enterprise Architecture (EA): TOGAF or similar methodology expertise; ability to map current and target state architectures; ability to document reference architectures and design patterns.

  2. Artificial Intelligence: experience in developing AI strategies and AI integration in complex IT landscapes.

  3. AI/ML Solution Architecture: Practical AI solution implementation experience. Familiarity with AI/ML lifecycle, models, tools (e.g., ChatGPT Enterprise, Microsoft Copilot), and platforms.

  4. Systems Integration: Understanding of how various tools and platforms interoperate in a SaaS-heavy environment.

  5. Data Flow Mapping: Skill in designing and visualizing high-level data flows.

  6. Governance and Risk: Awareness of AI governance frameworks, compliance, and responsible AI principles.

  7. Business Alignment: Ability to align AI capabilities with business goals and functions.

  8. Strategic Planning: Ability to identify capability gaps and dependencies and develop roadmaps

  9. Change Management: Understanding of organizational change practices and risks in AI adoption.

  10. Risk Assessment: Knowledge of risks specific to AI systems, including scalability, bias, and explainability.

  11. Data Architecture: Expertise in structuring how data is sourced, stored, shared, and governed, especially for AI/ML workloads.

  12. Metadata & Domain Modeling: Ability to define data and document domains for various AI use cases.

  13. Data Governance: Familiarity with data sharing policies, privacy considerations, and security frameworks.

  14. Conversational & Autonomous AI: Knowledge of AI agent technologies, frameworks (e.g., Lang Chain, Crew AI, Auto Gen), and orchestration.

  15. Platform Evaluation: Experience evaluating and selecting AI platforms/tools against enterprise-grade requirements.

  16. Documentation & Guidelines Development: Ability to write clear strategic documentation and create guidelines.

  17. Strong Communication: Comfortable interacting with both technical teams and business stakeholders.

  18. Workshop Facilitation: Ability to run discovery sessions, interviews, and design workshops.

  19. Stakeholder Management: Experience working in cross-functional enterprise environments with diverse stakeholder groups.

  20. Collaboration: Highly collaborative, with a bias towards action and delivery.