Senior AI Architect

Your Opportunity:

Health Shared Services – Technology Services has an exciting opportunity for a highly skilled Senior AI Architect to join the Technology Architecture & Solutions (TAS) team. The Senior AI Architect will play a key role in shaping and advancing the organization's enterprise AI architecture and technology strategy. This position will bridge business and clinical demands with enterprise technology capabilities, translating AI opportunities and strategic objectives into sustainable architecture, technology roadmaps and secure, scalable solutions. Working across on-premises, private cloud and public cloud environments, the Senior AI Architect will define and evolve the organization's hybrid AI architecture, enabling predictive and analytical AI/ML, Generative AI, Large Language Models (LLMs) and AI-enabled applications and agents. The position will provide architectural leadership from strategic planning and technology evaluation through solution architecture, implementation and production operationalization. It will establish architectural direction, reference architectures, standards and reusable patterns while ensuring alignment with enterprise architecture, infrastructure, data, security, privacy and operational requirements. The successful candidate will bring a combination of enterprise architecture experience, deep technical knowledge of modern AI platforms and technologies, and the ability to engage executive leaders, business stakeholders, architects, engineers and operational teams to translate organizational priorities into practical technology solutions.

Description:

Senior AI Architect is responsible for: AI strategy and roadmap. Translate business and clinical priorities into AI direction; maintain the AI target architecture, roadmap and investment priorities. Evaluate technologies, adoption models, build-versus-buy and sovereign AI options within the hybrid strategy. Hybrid AI platforms. Define architecture across on-premises, private and public clouds, including GPU infrastructure, model hosting, containers, cloud AI and integration. Set patterns for workload placement, deployment, orchestration and connectivity, addressing security, resilience, cost and data residency. AI demand and solution architecture. Assess clinical and business AI opportunities, translating needs into viable solutions with feasibility, data readiness and success criteria. Lead options analysis and architecture from proof of concept to production, aligned with enterprise standards. GenAI, LLM and agent architecture. Architect predictive/analytical AI, GenAI, LLM, RAG and agents; define patterns for model selection, inference, retrieval, orchestration and enterprise integration. AI security and governance. Embed security, privacy, responsible AI and risk management; define controls for identity, least privilege, data protection, prompt injection, agent/tool security, guardrails, human oversight, monitoring and auditability. Operations, performance and cost. Address production readiness, scalability, resilience, performance and lifecycle management, including MLOps/LLMOps, model evaluation, validation, monitoring, drift and rollback. Assess GPU capacity, AI consumption, licensing, inference costs and TCO. Architecture leadership. Collaborate across enterprise, application, data, infrastructure, cloud, cybersecurity and privacy teams; lead architecture reviews and guide key design decisions and risks.

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