• Enterprise Architect, AI & Intelligent Platforms

• Provide enterprise architecture leadership for AI, Generative AI, agentic systems, and intelligent platforms. This role defines the architectural foundation, governance, standards, and reference patterns required to scale AI safely, consistently, and strategically across the enterprise
• This is not a delivery support role. It is an enterprise architecture authority accountable for aligning AI adoption with business strategy, enterprise capabilities, risk appetite, regulatory obligations, and long-term operating model transformation
• The role establishes AI as a governed enterprise intelligence layer, embedded across platforms, processes, data, and decisioning, not deployed as isolated tools.

• Shape enterprise AI architecture
• Define target-state architecture for AI platforms, agents, data/context, integration, and operating model implications.
• Establish guardrails and governance
• Set standards, principles, reference architectures, and decision frameworks that enable safe, scalable AI adoption.
• Enable business transformation
• Translate business strategy into AI-enabled capability roadmaps that augment, automate, or rearchitect processes.
• De-risk adoption
• Ensure AI solutions meet requirements for security, privacy, model risk, resilience, explainability, auditability, and regulation.
• Drive platform and model optionality
• Guide decisions across MS AI Foundry, Azure OpenAI, M365 Copilot, OpenAI, Anthropic Claude, and other providers to balance value, cost, performance, and risk.

• The Enterprise Architect, AI & Intelligent Platforms, defines, governs, and evolves enterprise AI architecture in partnership with business, technology, data, cybersecurity, risk, privacy, compliance, and engineering teams
• The role provides architectural leadership across LLMs, RAG, AI agents, Copilot extensibility, orchestration, governance, observability, and Responsible AI, ensuring alignment with enterprise capability models, data strategy, cloud strategy, integration architecture, and operating model evolution.

• Enterprise AI Architecture Strategy
• Define and maintain the enterprise target-state architecture for AI and agentic systems
• Establish principles, standards, and decision frameworks for enterprise-wide adoption
• Translate business strategy into AI-enabled capability and investment roadmaps.

• Define architecture for MS AI Foundry, Azure OpenAI, M365 Copilot, Copilot Studio, Azure AI Search, and third-party LLM platforms
• Establish patterns for model orchestration, evaluation, guardrails, telemetry, cost, and lifecycle governance
• Define when to use Copilot, Foundry-based workflows, custom agents, or external models.

• Define enterprise patterns for AI agents, multi-agent workflows, tool use, and human-in-the-loop controls
• Establish agent governance, including registration, ownership, permissions, monitoring, auditability, and kill switches.

• Define enterprise context and RAG architectures to ensure grounded, explainable, secure AI
• Establish standards for AI-ready data, metadata, lineage, sensitivity, access, and provenance.

• Lead architecture for Copilot extensibility, Graph connectors, plugins, and enterprise integrations
• Define patterns for access control, DLP, identity, and information protection in Copilot workflows.

• Embed Responsible AI, model risk, cybersecurity, privacy, and regulatory controls into architecture patterns
• Define control overlays, risk tiering, monitoring, approval of workflows, and evidence capture.

• Lead AI architecture governance through review boards, SDLC checkpoints, and design authorities
• Maintain reusable architecture assets, standards, and reference patterns.

• Partner with engineering and platform teams to ensure architectures are implemented correctly and production-ready
• Maintain architectural accountability from concept through production and lifecycle management.

• Enterprise AI target-state architecture
• AI platform and agentic reference architectures
• RAG and enterprise context standards
• M365 Copilot/Studio extensibility patterns
• Model selection and deployment decision frameworks
• AI risk and control overlays
• Agent registry and lifecycle governance
• AI observability and telemetry standards
• Architecture standards, guardrails, and review templates
• Business capability to technology roadmaps

• Bachelor’s degree with 7+ years in enterprise/solution architecture or AI/ML engineering; or equivalent combination of education and experience
• 3+ years hands-on with Azure OpenAI Service, Azure AI Studio, or MS AI Foundry, including production deployments
• Demonstrated experience designing cloud-based AI architectures (Azure/AWS/GCP) with modern integration patterns and API management
• Experience integrating LLM/GenAI capabilities into enterprise systems with production-grade monitoring, scaling, and failover
• Working knowledge of M365 Copilot extensibility (plugins, connectors, Graph API, Copilot Studio)
• Strong understanding of Responsible AI, data governance, cybersecurity, and AI risk controls in regulated environments.

The Company offers the following benefits for this position, subject to applicable eligibility requirements: medical insurance, dental insurance, vision insurance, 401(k) retirement plan, life insurance, long-term disability insurance, short-term disability insurance, paid parking/public transportation, paid time off, paid sick and safe time, hours of paid vacation time, weeks of paid parental leave, and paid holidays annually – as applicable.