We are seeking an AI Architect to design, govern, and evolve enterprise AI architectures that enable the adoption of Predictive AI, Generative AI, Agentic AI, and Intelligent Automation across the organization. This role provides architecture leadership throughout the AI solution lifecycle, ensuring that AI solutions are scalable, secure, responsible, compliant, and aligned to business outcomes and enterprise architecture standards. The AI Architect will work closely with business stakeholders, Enterprise Architecture, Data Architecture, Security, Platform Engineering, Governance, Risk, Compliance, and delivery teams to define architecture patterns, review solutions, establish standards, and guide implementation. Responsibilities Define and maintain enterprise AI reference architectures, blueprints, standards, and design patterns Shape solution architectures for AI use cases across predictive, generative, agentic, conversational, document intelligence, and automation domains Lead architecture design and solution shaping activities spanning data, model, application, orchestration, integration, and infrastructure layers Design patterns for RAG, agentic AI, multi-agent systems, prompt engineering, context engineering, AI orchestration, model serving, and knowledge management Conduct architecture reviews, provide design authority, and issue architecture sign-off for AI solutions Identify architecture risks, technical debt, and remediation opportunities, and maintain architecture decision records Implement architecture controls supporting Responsible AI, including content safety, hallucination mitigation, human-in-the-loop controls, and privacy protections Evaluate emerging AI platforms, tooling, and frameworks, and define technical direction for foundation models, agent platforms, vector databases, and MLOps/LLMOps/AgentOps capabilities Act as the primary liaison between business teams and the AI Center of Excellence, translating business priorities into AI architecture direction Facilitate architecture workshops, design sessions, and solution reviews with business and technical stakeholders Mentor architecture analysts, solution architects, and engineering teams Track emerging trends in AI and foundation models, and contribute to the evolution of the Enterprise AI Reference Architecture Requirements 8-15+ years of overall technology experience, including 5+ years in Solution Architecture, Enterprise Architecture, Data Architecture, Platform Architecture, or AI Architecture roles Experience delivering enterprise-scale AI platforms, AI-enabled transformation initiatives, or complex AI solution architectures, ideally within regulated, security-conscious, or governance-driven environments Expertise in designing enterprise-scale AI reference architectures across experience, agentic core, retrieval, orchestration, integration, models, data platform, and infrastructure layers Knowledge of predictive AI, generative AI, foundation models, agentic AI, RAG, vector and hybrid search, semantic layers, ontology management, and knowledge graphs Skills in designing agentic and application architectures, including agent runtimes, reasoning loops, tool and function calling, multi-agent orchestration, and memory/state management Experience defining AI guardrails and responsible AI controls, including content filtering, prompt-injection defense, PII detection and redaction, and audit logging Knowledge of integration and connectivity patterns, including API gateways, service mesh, event and message streaming, and protocol mediation such as MCP and A2A Understanding of model serving and lifecycle capabilities, including model catalogs, inference infrastructure, fine-tuning, and continuous evaluation Experience with MLOps, LLMOps, and AgentOps practices, including CI/CD for models and agents, monitoring, drift detection, and cost and usage metering Proficiency in Azure architecture, including landing zones, networking, identity and access management, and container platforms Proficiency in on-premises architecture, including data center hosting, virtualized infrastructure, GPU and accelerator capacity, and secure connectivity Understanding of enterprise data platform capabilities, including governed data products, feature and embedding stores, lineage, and data classification