Cloud Data & AI Platform Architect

The Cloud Data & AI Platform Architect plays a pivotal role in defining and driving the architecture, governance, adoption, and evolution of enterprise Data & AI platform capabilities. This individual is responsible for establishing platform standards, architecture principles, service offerings, and onboarding frameworks that enable Business Divisions to leverage shared Data, Analytics, and AI capabilities securely, efficiently, and consistently — while flexing support based on each division's maturity.

This role is central to scaling Cloud & Compute's platform footprint from Cloud Foundation to Data& AI over the next years, directly enabling the cloud roadmap's Data and AI Platform build-out. It ensures divisions — regardless of capability level — can adopt Fabric, Foundry, and broader Azure Data & AI services in a governed, consistent way.

Internal Relationships:

• Business Division IT teams
• Product Owners and Service Owners
• Solution Architects
• Enterprise Architecture
• Security, IAM, Risk & Compliance teams
• Data and AI teams

External Relationships:

• Microsoft and strategic technology partners
• Data and AI platform vendors
• Managed Service Providers
• Technology consultants and implementation partners

Major Responsibilities:

Platform Strategy & Roadmap:

• Define and maintain the Cloud Data & AI Platform vision, strategy, and roadmap.
• Establish platform services covering Data, Analytics, AI, and automation capabilities.
• Define service offerings and platform consumption models.
• Assess emerging technologies and recommend platform enhancements.
• Define the enterprise AI Gateway architecture and its capabilities.
• Define MLOps and LLMOps standards.
• Establish model lifecycle management processes.
• Define monitoring, retraining, drift detection, and model governance approaches.
• Support deployment patterns for predictive AI, GenAI, copilots, agents, and AI services.

Platform Architecture & Governance:

• Develop architecture principles, standards, and reference architectures.
• Define reusable patterns for analytics, data products, AI solutions, agents, copilots, and business automation.
• Establish platform governance, architecture review, and onboarding processes.
• Ensure alignment with enterprise architecture, security, and compliance requirements.
• Define security baselines for Data and AI services.
• Establish data protection, encryption, key management, and secret management standards.
• Define data residency and sovereignty controls.
• Define enterprise data governance standards, policies, and classification requirements in partnership with the Data Governance team.
• Establish requirements for data catalog, lineage, metadata, quality, and retention.
• Review Purview integration design for compliance with governance standards.

Business Divisions Engagement& Platform Adoption:

• Engage Business Divisions to understand Data & AI requirements and priorities.
• Lead discovery workshops and use-case assessments.
• Act as the primary subject matter expert and single point of contact for platform capabilities.
• Promote adoption of platform services and capabilities.

Divisional Enablement Strategy:

• Assess Business Division data & AI maturity and capability levels.
• Define a tiered engagement model — self-service for capable divisions, hands-on architecture support for divisions with limited capability.
• Design solution blueprints and accelerators to reduce implementation effort for low-maturity divisions.
• Define clear exit criteria for transitioning platform-supported use cases back to division ownership.

Financial Management & FinOps:

• Define platform cost governance, chargeback/showback approaches, and consumption policies.
• Review consumption trends and recommend optimization opportunities.
• Support investment planning and capacity forecasting.

Stakeholder Management:

• Communicate platform strategy, roadmaps, and investment priorities.
• Facilitate alignment between business and technology teams.