Chief AI Architect
Indexed description
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.
- 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.
- AI capabilities are consistently applied to high-value business use cases across AWM.
- Clear, reusable AI architecture patterns adopted across teams.
- AI systems operate with strong observability, quality, and governance.
- Business leaders have confidence in AI-driven decision support and automation.
- AWM is positioned to scale both traditional AI and generative AI capabilities responsibly.
- Defining GenAI and agent-based architecture patterns (e.g., RAG, orchestration layers, evaluation frameworks).
- Establishing AI observability and monitoring practices across all solutions.
- Driving AI adoption in advice workflows (advisor assist, client engagement, operations automation).
- Creating clear guidance on when and how to use AI vs. traditional approaches.
- Partnering with Data Architecture to ensure AI-ready data foundations.
- Integrating AI capabilities across multiple Vanguard systems and third-party platforms.
- Partnering with Enterprise AI & Engineering Platforms organization to shape enterprise AI reference architecture and technology that supports AWMT AI priorities
About Vanguard
At Vanguard, we don't just have a mission—we're on a mission.
To work for the long-term financial wellbeing of our clients. To lead through product and services that transform our clients' lives. To learn and develop our skills as individuals and as a team. From Malvern to Melbourne, our mission drives us forward and inspires us to be our best.
How We Work
Vanguard has implemented a hybrid working model for the majority of our crew members, designed to capture the benefits of enhanced flexibility while enabling in-person learning, collaboration, and connection. We believe our mission-driven and highly collaborative culture is a critical enabler to support long-term client outcomes and enrich the employee experience.
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