Principal Enterprise Data & AI Architect (AI)

Location: Saint Petersburg, Florida, United States; hybrid schedule with in-office work 2–3 days per week, averaging 10–12 days per month at the St. Petersburg corporate office.

Company

hirify.global is a financial-services firm focused on wealth management, brokerage, and asset management.

What you will do

• Define enterprise architecture strategy, target-state designs, reference architectures, implementation blueprints, technical guardrails, and engineering standards for data and AI platforms.
• Design cloud-based data architectures spanning operational stores, data warehouses, lakehouses, data products, semantic layers, vector stores, APIs, and governed data-access services.
• Lead modernization and migration from on-premises data platforms to scalable, governed, AI-ready cloud platforms.
• Develop architecture patterns for machine learning, generative AI, RAG, intelligent applications, workflow automation, and agentic AI.
• Evolve governed agentic data access into production-grade solutions with secure, entitled, audited tools and API adapters.
• Lead architecture reviews, define non-functional requirements, create migration roadmaps, and mentor engineers, architects, and delivery teams.

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, lakehouses, data products, metadata, lineage, data quality, semantic layers, and governed data access.
• Hands-on architecture experience with cloud data platforms such as AWS Redshift, Snowflake, Databricks, or Google BigQuery; operational platforms such as Aurora, PostgreSQL, and DynamoDB; and graph platforms such as Neo4j or Neptune.
• Strong AI/ML platform experience with AWS SageMaker, AWS Bedrock, vector databases, MLOps, LLMOps, agent patterns, prompt engineering, RAG, tool and API integration, orchestration, context, and memory management.
• Understanding of data and AI governance, privacy, security, access controls, auditability, regulatory expectations, model risk, and operational risk.
• Bachelor’s degree in Computer and Information Science or Computer Engineering, plus experience in wealth management, financial services, brokerage, or asset management.

Nice to have

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

Culture & Benefits

• Hybrid-friendly workstyle combining flexibility with in-office collaboration.
• Benefits may include medical, dental, vision, life, disability, critical illness, and accident insurance.
• Retirement savings, paid vacation, holidays, sick leave, and parental leave may be available.
• Less than 25% travel is expected.
• Guiding behaviors emphasize development, collaboration, pragmatic decision-making, ownership, delivery, and continuous improvement.