Principal Enterprise Data & AI Architect
$150,000–$200,000 year
HybridSt. Petersburg, Florida, United States
Job Summary
Define target-state architecture for modern cloud-based data and AI platforms, including operational data stores, cloud data warehouses, data lakehouses, semantic layers, and agentic data access services. Lead core data platform modernization by evaluating legacy capabilities, defining workload placement criteria, and guiding migration from on-premises to scalable, governed cloud platforms. Design scalable architecture patterns for data ingestion, transformation, storage, and consumption across batch, streaming, event-driven, real-time, analytics, machine learning, and generative AI use cases. Drive architecture reviews to identify design risks, integration gaps, and governance needs while translating complex business requirements into practical roadmaps and reusable engineering frameworks. Partner with Enterprise Architecture, data engineering, AI execution teams, and business stakeholders to establish technical guardrails and engineering standards for trusted, production-grade AI solutions.
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 BigQuery 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
- Strong understanding of data governance, AI governance, privacy, security, access controls, auditability, regulatory expectations, model risk, and operational risk in enterprise environments
- Experience designing AI-ready data architectures that support analytics, machine learning, generative AI, enterprise search, intelligent applications, AI agents, and operational AI use cases
- Ability to influence senior stakeholders and explain complex data and AI architecture concepts clearly to technical and non-technical audiences
- Experience in wealth management, financial services, brokerage, asset management industries
- Bachelor's: Computer and Information Science
- Bachelor's: Computer Engineering
Desired Qualifications
- Familiarity with MCP-based tooling, Agent Harness or equivalent technologies is preferred
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