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Xplor

Vertical SaaS with embedded payments

Enterprise AI and Data Architect

Applied Machine LearningConfirmed live in the last 24 hours
Full-timeRemote in USA$173k - $193k /yr

About the Job

Requirements

  • 15+ years in software, data, AI, or platform engineering, including at least 7 years as an enterprise or principal architect.
  • Experience taking large-scale LLM or agentic systems into production, including orchestration, retrieval, evaluations, guardrails, model routing, and observability.
  • Deep experience in enterprise data architecture, ideally using Snowflake, including semantic layers, governed metrics, data modelling, entity resolution, data quality, and lineage.
  • Experience designing multi-tenant SaaS platforms with strict tenant isolation and identity propagation.
  • Experience working with regulated data and designing for compliance from the start.
  • Experience leading architecture across multiple products or business units, ideally after acquisitions and involving modern and legacy technology.
  • Ability to write production code and conduct code reviews.
  • Experience establishing architecture governance, including review boards, standards, and waivers, that teams use.
  • Understanding of AI costs, including token budgets, cost per task, and FinOps for AI workloads.
  • Ability to explain technical trade-offs to executives and the board in business terms.
  • Must be legally authorized to work in the United States; visa sponsorship is not available.

Responsibilities

  • Own the enterprise target architecture for data and AI, and publish and maintain reference architectures, standards, and decision records.
  • Set architectural principles for modularity, API-first design, interoperability, reuse, portability, and lifecycle management.
  • Determine which capabilities are central by default, become central after reuse is proven, remain with business units, or are jointly owned.
  • Provide guidance on which technologies, models, and patterns to use for different needs.
  • Maintain a forward-looking view of AI technologies, vendors, models, and frameworks.
  • 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 more than 130,000 businesses, including agents built by customers and partners.
  • Set release requirements for agents, including approvals, versioning, rollback, guardrails as policy code, and evaluation gates.
  • Set enterprise data standards on Snowflake for semantic layers, 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 data architecture for the industry insights product, covering rights, aggregation, consent, and lineage.
  • Turn standards into context for AI coding agents through repository instruction files, reusable skills and prompts, MCP tool definitions, and starter templates.
  • Build architecture rules into continuous integration through policy as code, fitness functions, scans on AI-generated code, and pre-release evaluation gates.
  • Define what coding and runtime agents can do in each environment, including tool allow-lists, least-privilege access, prohibitions on secrets or card data in prompts or logs, and risk-based human review.
  • Set patterns for wrapping legacy products with MCP and for AI-assisted migration.
  • Track delivery metrics, including pull-request-to-production rate, rework within 21 days, change failure rate, and AI cost per accepted change.
  • Work with the AI Security Architect, CISO, Legal, and Privacy to build security into reference architectures, including threat models, least privilege, tenant isolation, prompt-injection defences, and server-side action validation.
  • Ensure 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.
  • Design platform cost controls, including cost tracking by business unit, product, and workflow; token and latency budgets; caching; and kill switches.
  • Optimize 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.
  • Establish 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, and present trade-offs to the Executive AI Steering group.
  • Lead technical evaluation of AI platforms, models, and data tools, and reduce overlap and lock-in through a gateway, abstraction layers, and a tested exit plan.
  • Set technical standards for consultants, including contributing code to Xplor repositories, pairing with engineers, and transferring knowledge.
  • Advise the Chief AI Officer on architecture, technology investment, and emerging risk in business terms.
  • Lead the community of business-unit architects and mentor engineers.

Desired Qualifications

  • Hands-on production experience with Model Context Protocol (MCP), agent frameworks, or an LLM gateway.
  • Experience leading a build-versus-buy decision for an AI platform, including contract, intellectual property, and exit terms.
  • Background in vertical SaaS.
  • Experience with Datadog or similar observability tools for AI systems, and OpenTelemetry.
  • Familiarity with AI risk frameworks such as NIST AI RMF and ISO/IEC 42001, and the EU AI Act.
  • Experience with data residency and privacy rules across Australia, New Zealand, the United States, the United Kingdom, and the European Union.
  • Experience modernizing legacy monoliths, including wrapping them with APIs for agent use.

About the Company

Xplor

Vertical SaaS with embedded payments

Xplor provides a global platform that combines software-as-a-service with embedded payments and tools for small and mid-sized businesses in education, fitness, field services, and personal services. It works by pairing vertical-specific management software with an integrated payments system called Xplor Pay, allowing businesses to run operations and take payments in one place. What sets Xplor apart is its focus on many different verticals, built into one brand through acquisitions, creating a single ecosystem rather than separate software or payment products. Its goal is to help SMBs grow recurring revenue and process more payments across many countries, aiming to serve about 130,000+ businesses and handle tens of billions in payments.

Company size
1,001-5,000
Company stage
Debt Financing
Total funding
Not listed
Headquarters
Atlanta, GA
Founded
2022

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