Enterprise AI and Data Architect

Xplor • Atlanta, GA

3d • Remote

About The Position

The Enterprise AI and Data Architect will own the enterprise architecture, standards, and shared platform for data and AI across Xplor Technologies’ seven business units. The role establishes clear guardrails for security, governance, and interoperability while defining what should be built centrally, by individual business units, or jointly. The architect is accountable for ensuring AI and data investments deliver measurable business results and lasting competitive advantage. This is a hands-on role, with 60–80% of time spent building.

The position covers enterprise data and AI architecture, agentic AI platforms, Snowflake data standards, AI-assisted development, security and responsible AI, cost management, observability, reliability, governance, vendor evaluation, and technical leadership. The role collaborates with the Chief AI Officer, AI Security Architect, CISO, Legal, Privacy, business-unit architects, and engineering teams. It chairs the fortnightly Data and AI Architecture, Security and Risk Council and advises the Executive AI Steering group. There is scope to grow a small team of 2–3 architects.

This is a fully remote role open to candidates anywhere in the United States except Hawaii and Alaska; Eastern or Central time zones are preferred.

Requirements

• 15+ years of experience in software, data, AI, or platform engineering, including at least 7 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 on 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 working with regulated data and designing for compliance from the start.
• Experience leading architecture across multiple products or business units, ideally after acquisitions and across modern and legacy technologies.
• Ability and willingness to write production code and conduct code reviews.
• Experience establishing architecture governance, such as review boards, standards, and waivers, that teams use in practice.
• 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 clear business terms.

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 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 so agents can use them.

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.
• Define which capabilities are centrally owned, business-unit owned, or jointly owned, and provide guidance on technologies, models, and patterns.
• Maintain a forward-looking view of AI technologies, vendors, models, and frameworks.
• Help shape the agentic AI reference architecture for model access, context management, orchestration, retrieval, evaluations, guardrails, identity, and telemetry.
• Design multi-tenancy patterns that support more than 130,000 businesses, including agents built by customers and partners.
• Set release requirements for agents, including approvals, versioning, rollback, policy-code guardrails, and evaluation gates.
• Set enterprise Snowflake data standards covering the semantic layer, governed metrics, data contracts, quality rules, lineage, retention, and deletion.
• Define the split between central data standards and business-unit domain models, and review domain models for consistency.
• Define technical criteria for data readiness used in product planning.
• Work with Legal and Privacy on data architecture for the industry insights product, including rights, aggregation, consent, and lineage.
• Turn standards into context for AI coding agents, including repository instruction files, reusable skills and prompts, MCP tool definitions, and starter templates.
• Build architecture rules into CI through policy as code, fitness functions, scans on AI-generated code, and pre-release evaluation gates.
• Define permitted coding- and runtime-agent actions in each environment, including tool allow-lists, least-privilege access, protections against secrets or card data appearing 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 such as PR-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 incorporate threat models, least privilege, tenant isolation, prompt-injection defenses, and server-side action validation into reference architectures.
• Ensure 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 usage, 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 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 evaluations of AI platforms, models, and data tools, and reduce overlap and lock-in through approaches such as a gateway, abstraction layers, and a tested exit plan.
• Set technical standards for consultants, including contributing code in 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 2–3 architects.

Benefits

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

What This Job Offers

• Job Type: Full-time
• Career Level: Senior
• Education Level: No Education Listed