Principal Enterprise Data & AI Architect

** _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 lake houses, 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 architecture, including data engineering, data lakehouse architecture, data lakes, data products, metadata, lineage, data quality, semantic layers, and governed data access.

+ Strong engineering and architecture experience with analytical/AI cloud-based data platforms such as AWS Redshift, Snowflake, Databricks, Google Big Query or comparable technologies.

+ Strong engineering and architecture experience with operational cloud-based data platforms such as Aurora, Postgres, Dynamo DB and Graph data platforms such as Neo4J, Neptune and related technologies.

+ Strong AI/ML platform engineering and architecture experience with AWS Sagemaker, AWS Bedrock , Vector databases like Open Search, ML Ops and LLM Ops

+ Experience defining agent design patterns, AI/data reference architectures, reusable frameworks, technical guardrails, engineering standards, and production-ready architecture patterns.

+ Deep expertise in agentic AI and LLM application architecture, including cloud-native AI/ML platform integration, model selection, prompt engineering, retrieval-augmented generation, tool/API integration, context and memory management, orchestration patterns, and production-grade frameworks for building scalable AI solutions. Familiarity with MCP-based tooling, Agent Harness or equivalent technologies is preferred.

+ Strong understanding of data governance, AI governance, privacy, security, access controls, auditability, regulatory expectations, model risk, and operational risk in enterprise environments.

+ Experience…