Enterprise AI and Data Architect

Xplor Technologies powers the experiences at the heart of everyday life. Through modern vertical software, embedded payments, and AI-powered capabilities, we help businesses in fitness, recreation, golf and club, field services, laundry, education, and other membership-based and service-based industries simplify operations, uncover insights, and elevate customer and member experiences.

About the opportunity

You’ll be the enterprise architect for data and AI across all seven of our business units. You’ll set the architecture, standards and shared platform that every team builds on, so business units can focus on building products that set them apart, within clear guardrails for security, governance and interoperability.

A big part of the job is drawing the line between what we build once centrally (the shared foundation, platform services and standards) and what each business unit owns and builds for itself. You’ll be accountable for making sure our AI and data investments deliver measurable business results and a lasting competitive advantage.

This is a hands-on role. You’ll spend 60 to 80% of your time building.

What you’ll do

Architecture and standards

• 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, including agents built by customers and partners
• 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, and review those models for consistency
• Define the technical criteria for data readiness used in product planning
• Work with Legal and Privacy on the data architecture for our industry insights product, covering rights, aggregation, consent and lineage

Standards for AI-assisted development

• Turn standards into context that AI coding agents use: repository instruction files, reusable skills and prompts, MCP tool definitions and starter templates
• Build architecture rules into CI: policy as code, fitness functions, scans on AI-generated code, and evaluation gates before release
• Define what coding and runtime agents can do in each environment, including tool allow-lists, least-privilege access, no secrets or card data in prompts or logs, and human review based on risk
• Set patterns for wrapping legacy products with MCP and for AI-assisted migration
• Track delivery metrics such as PR-to-production rate, rework within 21 days, change failure rate and AI cost per accepted change

Security, privacy and responsible AI

• Work with the AI Security Architect, CISO, Legal and Privacy to build security into every reference architecture: threat models, least privilege, tenant isolation, prompt-injection defences and server-side action validation
• Make sure the architecture supports data residency, tenant isolation, retention, deletion and contractual data-use limits
• Assess risks from model providers, external AI services, open-source models, training data, dependencies and third-party integrations

Cost, observability and reliability

• Design cost control into the platform: cost tracking by business unit, product and workflow, token and latency budgets, caching and kill switches
• Optimise token use, model choice, context size, retrieval efficiency and compute
• Define a standard telemetry schema in Datadog covering outcome, quality, cost and security signals
• Set service-level objectives for central platform services

Governance and vendors

• Set up an AI and data governance framework covering ownership, a…