Jobs and Careers

Principal Enterprise Data & AI Architect

St. Petersburg, United Statesfull_timeVerifiedPosted 9 Jul 2026

About the role

Job Description Summary

We are seeking a highly experienced Principal Enterprise Data & AI Architect to define, evolve, and operationalize architecture across enterprise data platforms and AI/ML platform capabilities. The candidate must be a very strong Data & AI architect with deep hands-on engineering credibility in cloud-based data platforms, enterprise-scale AI architecture, and production-grade reference frameworks and solution design. The Architect will advance governed agentic data-access architecture from reference design to working production-grade solutions. The architect will establish reusable agent design patterns, Data/AI reference architectures, and engineering frameworks that enable delivery teams and business teams to adopt AI capabilities safely, consistently, and at scale.
The successful candidate will connect trusted enterprise data with scalable AI execution. They will define how data platforms, semantic models, ontologies, knowledge graphs, AI agents, governance controls, and engineering standards work together to support analytics, advanced analytics, AI-enabled business workflows, and self-service data access.

Job Description

This position follows our hybrid-friendly schedule, so you get the best of both worlds – flexibility and collaboration. In office days will be 2-3 per week averaging 10-12 days per month in our St Petersburg, FL Corporate Office.

Key Responsibilities and Essential Duties

Serve as the principal enterprise architect for enterprise data platforms and AI/ML platforms/capabilities, agentic data access, semantic enablement, and data engineering standards.
Own the architecture strategy, target-state designs, reference architectures, implementation blueprints, technical guardrails, and engineering standards for trusted, governed, scalable data and AI capabilities
Define target-state architecture for modern cloud-based data and AI platforms, including operational data stores, cloud data warehouses, data lakehouses, data products, semantic layers, AI/ML platforms, vector stores, APIs, agentic data access services, and governed data consumption capabilities.
Lead core data platform modernization by evaluating legacy and modern platform capabilities, defining workload placement criteria, and guiding migration from on-premises data platforms to scalable, governed, AI-ready cloud platforms.
Evaluate and recommend cloud data, analytics, AI, semantic, governance, and engineering technologies using decision criteria based on scalability, security, interoperability, performance, resilience, cost, supportability, and enterprise fit.
Design scalable architecture patterns for data ingestion, transformation, storage, curation, publishing, retrieval, and consumption across batch, streaming, event-driven, real-time, analytics, machine learning, generative AI, and agentic use cases.
Define architecture patterns for machine learning, generative AI, AI services, intelligent applications, AI-enabled analytics, retrieval-augmented generation, workflow automation, and agentic AI solutions.
Evolve governed agentic data-access architecture from reference design to production-grade implementation, including agent-safe tools and API adapters that are read-optimized, entitled, audited, secure, and appropriate for regulated enterprise use.
Drive architecture reviews for data and AI initiatives, identifying design risks, integration gaps, scalability concerns, governance needs, operational readiness issues, supportability gaps, and opportunities for reuse.
Define non-functional requirements for data and AI solutions, including scalability, performance, latency, availability, resilience, observability, maintainability, cost efficiency, and operational supportability.
Translate complex business, data, and AI requirements into practical architecture roadmaps, implementation patterns, reusable engineering frameworks, and migration plans.
Partner closely with Enterprise Architecture, Enterprise Data & Analytics, AI execution teams, data engineering, data science, analytics, cloud/platform engineering, application teams, security, risk, compliance, governance, and business stakeholders.
Mentor engineers, architects, and delivery teams on architecture patterns, AI/data design practices, engineering standards, operational readiness, and production-grade solution delivery.

Required Qualifications

15+ years of experience in data architecture, enterprise architecture, cloud data architecture, data engineering architecture, AI architecture, ML architecture, or related senior technology roles.
Deep expertise in enterprise data