Enterprise AI Architect

• 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

medical, dental and vision coverage, incentive and recognition programs, life insurance, and 401k contributions