Enterprise Architect
This role serves as a senior AI architecture leader responsible for defining enterprise patterns, standards, and decision frameworks that guide the adoption of AI across Global Technology. The role provides architectural leadership spanning AI platforms, agentic systems, retrieval architectures, model integration, workflow automation, and enterprise AI governance. Working closely with CIO architecture teams, platform engineering, security, and risk partners, this role helps translate rapidly evolving AI capabilities into practical, scalable, and governed enterprise solutions.
Must Have
- Enterprise AI Architecture, AI/ML, LLMs, and Generative AI.
- Agentic AI, Multi-Agent Systems, Agent Orchestration, and MCP.
- RAG, Embeddings, Vector Databases, and Semantic Search.
- Azure AI Foundry, Azure OpenAI, Copilot Studio, and AI Model Integration.
- AI Governance, Responsible AI, Security, and Model Lifecycle Management.
Nice to Have
- Experience with AWS AI services, including Amazon Bedrock and SageMaker.
- Knowledge of knowledge graphs and enterprise retrieval architectures.
- Experience with GitHub Copilot and enterprise AI ecosystem capabilities.
- Familiarity with emerging AI interoperability standards and agent-to-agent communication.
- Experience defining enterprise AI reference architectures and reusable design patterns.
- Experience working in large-scale, highly regulated enterprise environments.
- Understanding of deterministic, probabilistic, and hybrid automation approaches.
- Experience collaborating with CIO architecture teams, platform engineering, security, and risk stakeholders.
Responsibilities
- Define enterprise AI architecture strategy, standards, reference architectures, and reusable design patterns.
- Establish architecture decision frameworks for AI platform selection, model integration, solution design, and deployment.
- Provide architectural leadership for Generative AI, agentic AI, multi-agent systems, RAG, and enterprise AI platforms.
- Define standards for agent orchestration, planning, memory, tool invocation, and human oversight.
- Drive AI interoperability standards, including MCP, agent-to-agent communication, and enterprise workflow integration.
- Establish architecture patterns for vector databases, semantic search, embeddings, and enterprise knowledge retrieval.
- Guide adoption and integration of Azure AI Foundry, Azure OpenAI, Copilot Studio, AWS Bedrock, and equivalent technologies.
- Define AI governance frameworks, security guardrails, responsible AI practices, and model lifecycle management standards.
- Align AI architecture with enterprise security, privacy, regulatory, risk, and compliance requirements.
- Partner with platform engineering, security, risk, and CIO architecture teams to ensure scalable and governed AI adoption.
- Drive consistency and reuse through enterprise AI architecture patterns, templates, and standards.
- Translate complex AI concepts into actionable architectural recommendations for technical and business stakeholders.
- Influence senior leadership and drive architecture decisions across multiple enterprise initiatives.
- Promote responsible and scalable AI adoption through enterprise-wide architectural governance and best practices.