Jobs in Hawaii 2026 (Now Hiring) - Smart Auto Apply

The Director, Enterprise Architecture turns AI and data strategy into practical business process, application, integration, data, AI, and technology designs for Hawaii Mobility. Reporting to the VP, Data & AI, this role guides target architectures and transition roadmaps that strengthen customer relationships, connect journeys, and improve daily operations. Design authority centers on Hawaii Mobility, with partnership across Australia mobility, music, and investments where shared capabilities make sense. The Director leads the Data & AI Architect and works with operators and delivery teams to redesign workflows and connect digital and physical experiences, running a lean architecture practice: listening first, building clear guardrails and reusable paths, and learning quickly from production while helping teams adopt change at a practical pace.KEY OUTCOMES:Enterprise Architecture Strategy & Governance

• Translate Servco Digital Strategy principles of Omnichannel Integration, Datafication, and Tech Modernization into connected customer journeys, governed 360 data, and simplified, open and cloud-native target architectures. Keep reference patterns small, actionable, and informed by production learning.

• Apply Problem Led, Highly Automated, and Talent Driven principles with operations and technology teams. Use risk-tiered reviews, brief decision records, supported build paths, and local decisions within guardrails; stage changes to match operational readiness.

• Listen to frontline teams and leaders; map customer journeys, business processes, capabilities, and system dependencies across sales, service, and fulfillment. Use systems thinking and first principles to find root constraints, test assumptions, and design practical transitions rather than carry forward avoidable complexity. Advise the VP, Data & AI on tradeoffs and outcomes.

AI Delivery & Production Architecture

• Design how predictive AI, generative AI, agents, and automation fit operational workflows. Set reusable patterns for model choice, governed retrieval and context, scoped tool and API access, agent orchestration, human handoffs, and integration with enterprise applications.

• Architect identity, permissions, approval points, traceability, evaluation, monitoring, fallback, and recovery for AI-assisted decisions. Turn NIST AI RMF, its Generative AI Profile, and ISO/IEC 42001 concepts into practical risk controls.

• Establish MLOps and LLMOps patterns from experiment to production: versioned data, models, prompts, and tools; automated tests and releases; offline and live evaluation; tracing, drift and safety monitoring, rollback, and iteration. Track quality, latency, cost, adoption, and business outcomes.

Connected Application & Data Architecture

• Shape application and integration architecture across APIs, events, services, and the system portfolio, linking discovery, vehicle inventory, trade, financing, sales, logistics, delivery, service, and support. Connect process changes and data flows so customer and operator experiences work across digital and in-person touchpoints.

• Define shared customer identity and consent, vehicle, inventory, transaction, service, and finance data models and contracts. Guide trusted 360 data products and Databricks/Azure architecture with ownership, quality, lineage, privacy, access, and semantic definitions; partner with Technology Infrastructure & Security on resilience and cost.

Partnership & Operator Enablement

• Lead and develop the Data & AI Architect and align engineers and delivery teams through practical design reviews. Pair with operators to test patterns in real work, teach rather than prescribe, invite feedback, share credit, and turn successful designs into supported build paths.

• Partner with architecture and technology leaders across Australia mobility, music, and investments on compatible patterns and reusable solutions, respecting each business's decision rights. Communicate dependencies, risks, costs, and choices clearly to business and technical leaders.

QUALIFICATIONS:Education:

• Bachelor's or Master's in a related field, or equivalent demonstrated experience.

Work Experience:

• 10-15 years of enterprise architecture experience with hands-on depth across application, integration, data, and process architecture, plus the ability to connect these domains to business capabilities, AI, and infrastructure.

• Production AI and automation architecture experience, including MLOps and LLMOps, governed tools and data, evaluation, observability, security, reliability, cost, an