VP, Data & AI Architecture

Raymond James•Saint Petersburg, FL

1d•Hybrid

About The Position

The VP, Data & AI Architecture is a senior individual-contributor technology leader accountable for the enterprise architecture and roadmap that govern AI and turn data into trusted insights at scale. The role defines the governed path AI use cases follow to production, the inventory and evidence supporting AI Governance and Model Risk Management, and the data products and shared metrics that provide consistent answers to advisors, clients, and the business. The position aims to accelerate safe AI adoption while meeting financial-services model-risk, privacy, and regulatory expectations. This is an AI-forward practitioner role. The leader uses AI coding agents such as Claude Code in daily work, including data discovery, lineage mapping, evaluation harnesses, and architecture documentation. They apply practical knowledge of generative AI and agent limitations, and build governance into platforms through defaults and automated checks. The role has no direct reports. It partners with data, analytics, and AI platform teams, which retain build-and-run ownership, and with AI Governance, Model Risk Management, Compliance, and Privacy teams, which retain policy and approval ownership. It requires executive presence and the ability to discuss technical architecture with engineers and risk with Compliance. The position follows a hybrid-friendly schedule, with three office days per week, including a Monday or Friday, at the St. Petersburg, Florida corporate office. Travel is less than 25%. The role is not eligible for work visa sponsorship, currently or in the future. Compensation includes base salary or wages and may include additional compensation, discretionary bonuses, or commissions. Benefits may include medical, dental, and vision coverage; life, critical illness, accident, and disability insurance; retirement savings; paid time off; and parental leave. Eligibility and specific offerings may vary by position and employment status.

Requirements

15+ years of hands-on depth across data platforms, analytics, data governance, and machine learning, including generative AI in production.
Experience at a large technology company or major regulated financial services firm putting AI into production under model-risk, privacy, and regulatory oversight.
Practical experience running an AI governance framework, not only writing policy.
Daily use of Claude Code or similar agentic tools, with the ability to demonstrate how AI improved work speed or quality.
Experience delivering data products or a shared metrics layer adopted across many business lines.
Ability to write SQL and Python and defend data or model decisions in detail with engineers, Risk, and Compliance.
Completed bachelor's degree in Computer and Information Science.
Strong executive presence and ability to engage in deep technical and risk discussions.

Nice To Haves

Professional-level cloud data or machine learning certification.
Certified Data Management Professional (CDMP) certification.
IAPP AIGP certification.

Responsibilities

Define and maintain the enterprise data and AI target architecture and roadmap, covering data platforms, data products, streaming data, the shared metrics layer, and machine learning and generative AI platforms.
Architect a governed platform path for every AI use case, including the AI gateway, use-case intake, prompt and model registry, evaluation, and monitoring, with controls implemented as defaults and automated checks.
Own the AI and model inventory architecture so every AI feature, model, and agent is discoverable, risk-tiered, and linked to its data, owners, approvals, and validation evidence.
Set standards for production AI testing and monitoring, including evaluation sets, regression gates for each change, drift monitoring, cost budgets, and tested non-AI fallbacks for advisor- and client-facing features.
Architect safe agentic AI through agent identity, least-privilege access, scoped tool and data permissions, and complete audit trails.
Define the data product and ownership model, ensuring each critical dataset has an owning domain, data contract, quality targets, and lineage.
Architect insight delivery at scale so key metrics have consistent definitions across dashboards, advisor tools, APIs, and AI assistants.
Architect enterprise search and retrieval-augmented generation over governed content, enforcing entitlements at retrieval and citing sources.
Embed privacy by design with Privacy and Information Security, including classification, minimization, retention, masking, and controls keeping client data out of non-production systems and model training unless approved.
Use AI coding agents hands-on to accelerate data and AI architecture work and establish safe-use patterns.
Run architecture reviews for major data and AI decisions, record decisions, and track exceptions through closure.
Measure results, including AI use cases in production with complete governance evidence, reuse of certified data products, and time to trusted insight.
Collaborate with senior executives on strategy, investment cases, and communications.
Maintain a strong external view of data and AI platforms, model providers, AI governance standards, and financial-services AI regulation.