Description
You’ll be the enterprise architect for data and AI across all seven business units at Xplor. You’ll set the architecture, standards, and shared platform that every team builds on, ensuring business units can focus on building products that set them apart within clear guardrails for security, governance, and interoperability.
Responsibilities:
- Own the enterprise target architecture for data and AI, and publish and maintain reference architectures, standards, and decision records.
- Set architectural principles: modularity, API-first design, interoperability, reuse, portability, and lifecycle management.
- Decide what’s central by default, what becomes central once reuse is proven, what stays with business units, and what’s jointly owned.
- Give clear guidance on which technologies, models, and patterns to use for which job.
- Keep a forward-looking view of AI technologies, vendors, models, and frameworks.
Agentic AI platform:
- Help shape the reference architecture for model access, context management, orchestration, retrieval, evaluations, guardrails, identity, and telemetry.
- Help design multi-tenancy patterns that scale to 130,000+ businesses.
- Set release requirements for agents: approvals, versioning, rollback, guardrails as policy code, and evaluation gates.
Data architecture:
- Set enterprise data standards on Snowflake: semantic layer, governed metrics, data contracts, quality rules, lineage, retention, and deletion.
- Define the split between central standards and business-unit domain models.
- Review those models for consistency.
Standards for AI-assisted development:
- Turn standards into context that AI coding agents use.
- Build architecture rules into CI: policy as code, fitness functions, scans on AI-generated code, and evaluation gates before release.
Security, privacy, and responsible AI:
- Work with the AI Security Architect, CISO, Legal, and Privacy to build security into every reference architecture.
- Ensure the architecture supports data residency, tenant isolation, retention, deletion, and contractual data-use limits.
Cost, observability, and reliability:
- Design cost control into the platform.
- Optimize token use, model choice, context size, retrieval efficiency, and compute.
Governance and vendors:
- Set up an AI and data governance framework covering ownership, accountability, decision rights, data quality, and escalation.
- Chair the fortnightly Data and AI Architecture, Security, and Risk Council.
Leadership and mentoring:
- Advise the Chief AI Officer on architecture, technology investment, and emerging risk.
- Lead the community of business-unit architects and mentor engineers.
This position has an annual base salary range of $173,000 to $193,000 USD.