Enterprise AI and Data Architect

Xplor•Atlanta, GA

8h•$173,000 - $193,000•Remote

About The Position

Xplor Technologies is hiring an Enterprise AI and Data Architect to set the enterprise architecture, standards, and shared platform for data and AI across all seven business units. The role defines what is built centrally, what is owned by business units, and what is jointly managed, while ensuring investments deliver measurable business results and competitive advantage. This is a hands-on individual contributor position, with 60–80% of time spent building. The role is fully remote within the United States, excluding Hawaii and Alaska; candidates in Eastern or Central time zones are preferred. The architect owns the enterprise target architecture and reference standards for data and AI; helps shape an agentic AI platform designed for scale and multi-tenancy; and establishes data standards on Snowflake. The role also embeds architecture and security requirements into AI-assisted development, CI processes, platform operations, governance, vendor selection, and responsible AI practices. It works closely with the AI Security Architect, CISO, Legal, Privacy, business-unit architects, and executive groups, and advises the Chief AI Officer. There is scope to grow a small team of two to three architects. The position offers an annual base salary of $173,000 to $193,000 USD. Xplor is a global software and payments company serving fitness, recreation, golf and club, field services, laundry, education, and other membership- and service-based industries.

Requirements

15 or more years of experience in software, data, AI, or platform engineering, including at least seven years as an enterprise or principal architect.
Experience taking LLM or agentic systems into production at scale, including orchestration, retrieval, evaluations, guardrails, model routing, and observability.
Deep experience in enterprise data architecture, ideally with Snowflake, including semantic layers, governed metrics, data modeling, entity resolution, data quality, and lineage.
Experience designing multi-tenant SaaS platforms with strict tenant isolation and identity propagation.
Experience with regulated data and designing for compliance from the start.
Experience leading architecture across multiple products or business units, ideally following acquisitions and involving both modern and legacy technology.
Ability to write production code and conduct code reviews.
Experience establishing architecture governance, such as review boards, standards, and waivers, that teams actually 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 plain business terms.
Legally authorized to work in the country where the role is advertised; Xplor does not sponsor visas.
Submit an application through the careers portal or an external job board; include the word “moonshot” at the top of the message to the Hiring Manager.

Nice To Haves

Hands-on production experience with Model Context Protocol (MCP), agent frameworks, or an LLM gateway.
Experience leading an AI platform build-versus-buy decision, including contract, intellectual property, and exit terms.
Background in vertical SaaS.
Experience with Datadog or similar AI observability tools 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.

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 and standards should be central, business-unit owned, or jointly owned, and when successful reuse justifies centralization.
Guide technology, model, and pattern choices, and maintain a forward-looking view of AI technologies, vendors, models, and frameworks.
Help shape reference architecture for model access, context management, orchestration, retrieval, evaluations, guardrails, identity, and telemetry.
Design multi-tenancy patterns capable of supporting more than 130,000 businesses, including agents built by customers and partners.
Set agent release requirements, including approvals, versioning, rollback, policy-code guardrails, and evaluation gates.
Establish enterprise Snowflake data standards for the semantic layer, governed metrics, data contracts, quality rules, lineage, retention, and deletion.
Define the division between central data standards and business-unit domain models, and review domain models for consistency.
Define technical data-readiness criteria for product planning.
Work with Legal and Privacy on data architecture for the industry insights product, including rights, aggregation, consent, and lineage.
Make architecture standards available to AI coding agents through repository instruction files, reusable skills and prompts, MCP tool definitions, and starter templates.
Build architecture rules into CI using policy as code, fitness functions, scans on AI-generated code, and pre-release evaluation gates.
Define permissions and controls for coding and runtime agents in each environment, including tool allow-lists, least-privilege access, restrictions on secrets and 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 PR-to-production rate, rework within 21 days, change failure rate, and AI cost per accepted change.
Partner with the AI Security Architect, CISO, Legal, and Privacy to incorporate threat models, least privilege, tenant isolation, prompt-injection defenses, and server-side action validation into reference architectures.
Ensure the architecture supports data residency, tenant isolation, retention, deletion, and contractual data-use limits.
Assess risks involving 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 selection, context size, retrieval efficiency, and compute.
Define a standard Datadog telemetry schema covering outcome, quality, cost, and security signals, and set service-level objectives for central platform services.
Establish AI and data governance 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 evaluations of AI platforms, models, and data tools, and reduce overlap and lock-in through approaches such as a shared 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, with scope to grow a team of two to three architects.
Continue hands-on production coding and provide code reviews respected by engineers.

Benefits

Paid parental leave programs.
Three extra days off to volunteer and give back to the local community.
Diversity and inclusion initiatives, including a D&I Council and Global Mentorship Program.
Access to free mental health support.
Flexible working arrangements.

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