Enterprise Technology, Chief Architect

Raymond James•Saint Petersburg, FL

18h•Hybrid

About The Position

The Enterprise Technology Chief Architect (ETCA) is responsible for defining, governing, and evolving enterprise-wide technology architecture across Raymond James’ broker‑dealer and RIA businesses, including the firm’s AI and advanced analytics capabilities. This role partners at all levels of the organization to develop, implement, and execute an organization-wide IT strategy that optimizes employee capabilities, achieves the organization's strategic objectives, and delivers competitive advantage. The ETCA ensures that all technology—including AI solutions—is secure, compliant, explainable, and aligned to advisor independence, fiduciary obligations, and regulatory expectations. The Chief Architect establishes architectural strategy and governance that enables innovation while protecting clients, advisors, and the firm.

Requirements

15+ years in enterprise technology or architecture
7+ years in senior architecture leadership roles
Deep experience in wealth management, broker‑dealer, or RIA environments
Experience governing AI or advanced analytics solutions in regulated settings
Strong command of Enterprise architecture frameworks (e.g., TOGAF)
Strong command of Wealth platforms and advisor ecosystems
Strong command of AI and data architectures, including generative AI
Strong command of Model governance, explainability, and risk controls
Ability to balance innovation, advisor autonomy, and compliance
Bachelor’s degree in Computer and Information Science
Bachelor’s degree in Data Science
Bachelor’s degree in Information Technology

Responsibilities

Define and maintain the enterprise technology and AI architecture vision, aligned to Raymond James’ wealth management strategy
Establish enterprise architecture principles, reference architectures, and design patterns across: Business applications, Data and analytics, Cloud and technology infrastructure, Security, Integrations and platforms, Artificial intelligence and machine learning solutions
Translate business and technology goals into target‑state architectures and multi‑year roadmaps, explicitly addressing AI enablement and risk
Guide strategic trade‑off decisions balancing innovation, advisor experience, regulatory risk, explainability, and operational resilience
Define the enterprise AI architecture strategy, including: Model lifecycle management, Data sourcing and lineage, Model training, validation, deployment, and monitoring, Human‑in‑the‑loop and decision‑support patterns
Ensure AI solutions are: Transparent and explainable, Appropriately governed for fiduciary and regulatory use, Designed to augment—not replace—advisor judgment
Establish AI architectural standards for: Generative AI, Agentic AI, Predictive models, Advisor intelligence and automation tools, Client/business facing AI capabilities, AI for Tech solutions
Design and lead the enterprise architecture and AI governance framework, including: Decision rights and review authorities, Architecture and AI design review boards, Documentation, traceability, and exception management
Ensure AI governance aligns with: SEC and FINRA expectations, Fiduciary standards and suitability requirements, Model risk management and supervisory oversight
Provide architectural and AI oversight for: New AI use cases and model deployments, Third‑party and vendor‑embedded AI solutions, Material changes impacting clients, advisors, or compliance
Maintain auditable records of architectural and AI decisions in partnership with Risk and Compliance
Provide architectural leadership across core wealth capabilities, including: Advisor desktop and workstation platforms, Client onboarding, KYC/AML, and suitability, Portfolio management, trading, billing, and performance reporting, CRM, workflow, and practice management tools
Ensure architecture supports: Commission‑based, fee‑based, and advisory programs, Hybrid advisors and dual‑registrant workflows, Multi‑custodial and product models
Integrate AI capabilities in a controlled and compliant manner across platforms
Define enterprise data and AI architecture supporting: Advisor insights and productivity, Client personalization and segmentation, Portfolio analytics and risk monitoring, Supervisory surveillance and compliance reporting
Establish data governance ensuring: Data quality, lineage, and provenance for AI models, Privacy and information‑barrier protections, Regulatory defensibility of AI‑driven outcomes
Partner with Data, Risk, and Compliance teams to operationalize model risk management and explainability standards
Define enterprise integration standards enabling AI‑enabled platforms and fintech tools
Evaluate and govern: Third‑party AI solutions, Vendor‑embedded AI features, Advisor‑selected tools leveraging AI
Ensure ecosystem participation adheres to architectural, security, and AI governance standards
Lead architecture for hybrid and cloud environments supporting AI workloads
Ensure AI architectures support: Scalability and cost controls, Secure model hosting and data access, Business continuity and resiliency
Drive adoption of platform capabilities that safely operationalize AI at scale
Partner with the CISO and Risk leadership to embed: Security‑by‑design and privacy‑by‑design, Responsible AI controls, Auditability of AI decisions and outcomes
Ensure technology and AI architectures support: Supervisory oversight, Client protection and fiduciary obligations, Regulatory examinations and audits
Build and lead a federated enterprise architecture and AI architecture practice
Mentor senior architects and AI leaders across domains
Act as a trusted advisor to executive leadership on AI risk, opportunity, and readiness
Influence across decentralized business models through expertise and credibility