AI Architect

About the Role: We are seeking a visionary and strategic Agentic AI Architect to lead the design, strategy, and governance of our next-generation multi-agent AI ecosystem. This is a senior-level, high-impact role focused on creating the architectural blueprints for how we build, deploy, and manage sophisticated autonomous agents. You will be the principal authority on defining how our internal and external agents interact, communicate, and are governed. This role involves designing a scalable and secure framework for Agent-to-Agent (A2A) communication, leveraging cutting-edge standards like MCP (Model Context Protocol) and ACP (Agent Control Protocol). You will set the technical direction and standards for developing and integrating agents across a diverse stack of cloud services, including Azure AI Foundry, Microsoft Copilot Studio, GCP Agent Space, AWS Agent Core, and OpenAI models. A critical component of this role is establishing robust governance, security, and compliance frameworks to ensure all AI agents operate safely, securely, and ethically. Key Responsibilities: AI Ecosystem Architecture: Design and champion the end-to-end architecture for a robust, multi-agent AI system, encompassing both internal (e.g., workflow automation, data analysis) and external (e.g., customer service, partner integration) agents. Protocol & Standards Definition: Define and enforce enterprise-wide standards for agentic communication, context management, and control. Lead the strategic implementation and governance of protocols like MCP and ACP. Platform Governance: Evaluate, select, and govern the use of various cloud AI platforms (Azure AI Foundry, GCP Agent Space, AWS Agent Core, etc.) and foundational models to ensure optimal performance, security, and cost-effectiveness. Security & Governance Framework: Develop and implement a comprehensive governance and security framework for all agentic systems. This includes defining access controls, data handling policies, prompt security (e.g., mitigating Indirect Prompt Injection), and robust logging and monitoring strategies. Multi-Agent System Design: Architect complex multi-agent workflows, defining agent roles, permissions, and A2A interaction patterns to solve complex business problems. Integration Strategy: Design the integration of the agentic AI ecosystem with core business applications, data platforms (e.g., M365, SharePoint), and third-party services. Technical Leadership: Provide technical leadership and mentorship to engineering teams. Collaborate closely with product owners, data scientists, and security teams to translate business requirements into scalable and secure architectural blueprints. Innovation & Research: Stay at the forefront of agentic AI, researching new protocols, tools (like Microsoft Copilot Studio), and methodologies to drive continuous improvement and innovation. Requirements & Qualifications: Proven Experience: 15+ years of experience in a solutions architect, enterprise architect, or principal engineer role, with a specific focus on designing and deploying large-scale AI, ML, or distributed systems. Agentic AI Expertise: Deep, demonstrable understanding of agentic AI concepts, multi-agent systems (MAS), and emergent agent behaviors. Protocol Knowledge: Expert-level knowledge of AI and agent communication protocols, specifically MCP (Model Context Protocol), and familiarity with A2A and ACP (Agent Control Protocol) frameworks. Cloud AI Platform Fluency: Demonstrable experience architecting solutions on major cloud AI platforms. Must have deep expertise in at least two of the following: Azure AI (Foundry, Copilot Studio), Google Cloud (GCP Agent Space), AWS (Agent Core), or OpenAI APIs. AI Governance & Security: Strong understanding of AI governance frameworks (e.g., NIST AI RMF), AI security principles, and risk management. Experience in securing LLM-based applications is required. Systems Design: Expertise in modern systems design, microservices architecture, API-first development, data integration patterns, and event-driven architecture. Leadership & Communication: Exceptional communication skills with the ability to articulate complex technical concepts to diverse audiences, from C-level executives to junior engineers. Education: Bachelor’s or Master’s degree in Computer Science, AI, or a related technical field.