Principal Enterprise Data & AI Architect

Raymond James•Saint Petersburg, FL

5d•Hybrid

About The Position

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.

Requirements

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.
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 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 (Required)
Bachelor’s: Computer Engineering

Nice To Haves

Familiarity with MCP-based tooling, Agent Harness or equivalent technologies is preferred.

Responsibilities

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.