Job Title: Sr. Director, AI & Enterprise Architecture

 

Position Summary:

The Head of AI & Enterprise Architecture is a Senior Director level role that will define the AI strategy and govern the future‑state architecture for our organization. This role owns the strategic blueprint for our hybrid‑cloud environment, multitenant B2B SaaS/BPaaS products, internal applications, and emerging AI capabilities. The position bridges business strategy, product development, infrastructure, information security and engineering execution.

 

Essential Duties and Responsibilities:

Core Mandate

• Enable AI and modernization: Develop AI strategy and explicitly weave AI capabilities (AI/ML, Agentic AI) into the target architecture and multi‑year roadmap, alongside ongoing cloud and platform modernization.
• Align business strategy with architecture: Connect our business model and go‑to‑market strategy with a coherent, scalable technology and data architecture spanning customer‑facing products, internal tools, and platforms.
• Define and govern future state: Establish target‑state models across business capabilities, applications, data, integration, infrastructure, and AI/ML so major initiatives move the organization toward that blueprint rather than creating silos or technical debt.
• Operate as business partner: Act as a trusted advisor to product, engineering, security, and operations leadership, translating business imperatives into coherent, phased architecture and technology decisions.

Key Responsibilities

AI Strategy and Delivery

• Develop Enterprise AI strategy for Convey and subsidiary companies, owning the strategy across these organizations, working closely with each organization to align strategy to business needs
• Develop and own the AI use case roadmap, responsible for overall delivery and execution of the roadmap in coordination with applicable delivery teams
• Execute AI Governance model, working closely with IT and business leaders in conjunction with the Information Risk Management Committee to govern AI models, practices, and solutions

Target Architecture and Strategy

• Document the existing application landscape, infrastructure, information security architecture, data flows, and integration patterns across products (member management, benefits admin, billing, big‑data analytics) and internal systems.
• Define future‑state architecture across:
  • Business capabilities (enrollment, billing, member engagement, analytics, reporting, regulatory compliance)
  • Application portfolio (product suites vs. internal tools; consolidation opportunities; partner/third‑party integrations)
  • Data architecture (multitenant data models, warehouse/lakehouse platforms, real‑time analytics, MDM)
  • Technology stack (cloud platforms, container orchestration, API gateways, messaging, security/IAM, observability)
  • AI/ML enablement (model ops, feature stores, governance, PHI compliance, productization of AI insights)
  • Infrastructure and deployment (hybrid cloud, network segmentation, DR/resilience, cost optimization)
• Establish and maintain reference architectures and patterns for:
  • Multitenant SaaS design (data isolation, tenant routing, configurability, billing/metering)
  • Modern API design and integration (REST, event‑driven, FHIR/HL7, EDI)
  • Data integration and ETL/ELT modernization (e.g., from SSIS to cloud‑native pipelines)
  • Secure, compliant handling of PHI in hybrid‑cloud environments
  • MLOps and responsible AI deployment in regulated healthcare environment

Governance and Standards

• Establish and run an Architecture Review Board (ARB) to review major architectural changes, new technology proposals, and significant product/integration initiatives.
• Define and evolve standards for:
  • Cloud infrastructure (IaC, deployment automation, networking, identity/access)
  • Data and integration (data models, master data, APIs, event contracts)
  • Security and compliance (encryption, audit logging, PHI handling, regulatory requirements)
  • AI/ML governance (model training, validation, deployment, monitoring, bias mitigation)
  • Observability and cost management (logging, metrics, tracin