Enterprise AI Architect

Description and Requirements

The Enterprise AI Architect proactively and holistically works with enterprise delivery program leaders to define and maintain the architectural runway for business initiatives enabled by emerging AI technologies. This role leads the technical integration design supporting implementation of agentic AI systems, including autonomous and semi-autonomous agents, multi‑agent orchestration, Agent‑to‑Agent (A2A) communication patterns, and AI‑enabled business process transformation. The Architect partners with Enterprise Analytics, Security, and I&O programs to develop and govern AI‑focused architectural designs that incorporate Model Context Protocol (MCP)-based tool integrations, enterprise-grade Agentic Control Plane (ACP) governance capabilities, and secure, observable agent execution frameworks. This includes ensuring agents operate safely within enterprise boundaries, follow standardized integration patterns, and enhance business capability maturity. The role advances enterprise composability by enabling modular, reusable AI components-including agents, context services, tool interfaces, and orchestration layers-that adapt to rapidly evolving business needs. The Architect identifies emerging AI and agentic technology trends that enable future‑state business capabilities and optimizes business processes for AI‑native execution. Architectural decisions are facilitated through collaborative governance models, ensuring alignment with enterprise strategies and standards.

Duties & Responsibilities:

• Collaborates with all delivery programs to assess solution requirements, evaluate AI/ML technologies, and support model/vendor selection.
• Provides architectural guidance to business domain aligned delivered programs by defining and designing AI‑enabling technologies that support business capabilities, processes, and enterprise initiatives.
• Supports design and governance of enterprise agentic AI architectures, including autonomous agents, multi‑agent systems, A2A communication patterns, and agent orchestration frameworks.
• Leads integration of Model Context Protocol (MCP) to standardize tool, service, and data access across agents, ensuring secure, auditable, and consistent interactions.
• Architects and operationalizes an enterprise Agentic Control Plane (ACP) to manage agent lifecycle, permissions, safety constraints, observability, escalation pathways, and runtime governance.
• Recommends business processes modernization options using business process management (BPM) and process mining methodologies to support agent-driven or multi‑agent execution.
• Supports delivery teams in implementing AI solution architectures aligned with enterprise reference architecture, security requirements, and governance protocols.
• Stays current with developments in AI research, agentic systems, orchestration frameworks, LLM methodologies, and emerging enterprise AI technologies.
• Conducts technology-neutral due diligence to evaluate AI platforms, agent frameworks, MCP compatibility, ACP infrastructure, and enabling tools.
• Partners with business stakeholders and delivery leaders to deliver architectural runway for highly composable AI and agent capabilities.
• Creates and maintains capability models using capability-based planning and human‑centric design methods to support AI strategies.
• Leads analysis of healthcare industry and AI innovation trends, assessing business impact and recommending actionable strategies.
• Ensures AI agents adhere to governance, responsible AI principles, privacy expectations, and internal risk frameworks.
• Establishes KPIs and evaluation frameworks to measure agent performance, A2A interaction quality, ACP governance effectiveness, and business value realization.
• Coaches architects, product owners, delivery teams, and business partners to build competency in AI architecture and agentic design patterns.
• Additional duties as assigned.

• Master's or bachelor's degree in business, computer science, engineering, systems analysis, or related field, or equivalent experience.
• Minimum of two years of design and implementation experience in IT, including deep knowledge of AI/ML frameworks, AI development environments, cloud platforms, and big data technologies.
• Minimum of two years of experience in solution architecture development and delivery, including business architecture interpretation.
• Experience designing or implementing agent-based AI systems, autonomous agents, multi-agent orchestration, or workflow‑integrated AI components.
• Working knowledge of business process management (BPM), process modeling, and translating processes into agent-oriented workflows.
• Understanding of A2A communication principles, MCP-based tool integration, and foundational agent governance concepts.
• High School diploma or GED from an accredited institution

• Proficient in enterprise architecture tools and techniques such as strategy-on-a-page, strategic planning, and business model canvas.
• Exposure to governance and compliance disciplines, including privacy and security management.
• Demonstrates intellectual curiosity, integrity, and a commitment to responsible AI practices.
• Understanding of product management, agile methodologies, and development practices; ability to guide teams on architectural impacts, risks, and technical debt.
• Experience with AI/ML libraries and frameworks (TensorFlow, PyTorch, scikit-learn) and agent orchestration tools (e.g., LangGraph, AutoGen, CrewAI).
• Experience implementing or evaluating Model Context Protocol (MCP) integrations for enterprise AI agents.
• Experience designing or governing Agentic Control Plane (ACP) capabilities, including agent policies, role-based permissions, observability, and safety oversight.
• Experience with cloud IaaS providers (AWS, Azure), Kubernetes, Spark, Hadoop, or related technologies.
• Comprehensive understanding of health insurance business models, financial modeling, cost-benefit analysis, and risk management.
• Experience ensuring AI systems-including agents-conform to ethical AI and regulatory expectations.
• Ability to evaluate emerging AI and agentic technologies and integrate them effectively into enterprise ecosystems.