Chief AI Architect

About the role

The Chief AI Architect for Advice & Wealth Management shapes and evolves the AI architecture underpinning advice delivery, client experiences, operational efficiency, and decision intelligence. This role ensures generative AI capabilities are applied in a practical, business-focused way—balancing innovation with responsible AI practices, scalability, and operational excellence. Partnering closely with business, product, and engineering teams, the Chief AI Architect provides architectural leadership to define how AI is used across the portfolio, establishing patterns, guardrails, and platforms that enable sustainable adoption and measurable outcomes.

Key Accountabilities

- Define and execute the AI architecture roadmap for Advice & Wealth Management in partnership and collaboration with other organizations. - Align, lead and collaborate across organizational lines to move AI forward. - Establish architecture patterns for generative AI powered applications including deployment and monitoring. - Help business and product leaders understand how they can leverage AI to solve their business problems. - Influence enterprise guardrails and standards for responsible AI usage (evaluation, observability, model performance, and risk management). - Establish repeatable patterns for integrating AI into applications, workflows, and platforms. - Partner with data architecture to ensure AI-ready data foundations (quality, governance, lineage). - Guide teams on build vs. buy vs. partner decisions for AI capabilities and platforms. - Research emerging AI technologies and patterns; translate into practical recommendations and investment proposals. - Influence senior leadership on AI strategy, investment priorities, and sequencing. - Coach and mentor architects and senior engineers in AI architecture and design practices.

Experience & Capabilities

- Deep expertise in agentic application architectures, model selection, fine tuning, prompt engineering, agentic loop design, context management, memory, MCP and tool calling a must. - Proven ability to apply AI to real business use cases, moving solutions from concept to production. - Experience designing AI systems that are secure, observable, and scalable. - Strong understanding of model lifecycle management (training, deployment, monitoring, retraining). - Experience establishing responsible AI governance and controls. - Knowledge of AI/ML platforms, cloud-native patterns, and integration approaches (e.g. MCP, A2A, etc.). - Ability to influence senior stakeholders and collaborate across business and technology.