Lead AI Architect

• Location Name
  - Location Code:
  Home Office
  - Washington-DPIWA

Domino’s is a purpose-inspired, performance-driven company powered by exceptional people who are committed to feeding the power of possible—one pizza at a time. Founded in 1960 as a single store in Ypsilanti, Michigan, Domino’s has grown into the largest pizza company in the world, with more than 22,300 stores across 90+ markets. Our success is built on the strength of our people, technology, and franchise system.

As a leader in innovation, we continue to transform the pizza and QSR industry while creating opportunities for team members to learn, grow and thrive. If you're ready to make an impact, build a meaningful career and help shape what's next, there's a place for you at Domino's.

As Lead AI Architect, you own the technical architecture for Domino's enterprise AI platform – spanning classical AI, generative AI, and agentic AI. This is the senior individual-contributor technical authority on AI: you set the direction for how AI capabilities are built, served, orchestrated, and governed, and you drive outcomes through design authority and influence rather than direct reports. You own the reference architecture and the paved road teams build on, and the build-depth judgment for every incoming ask – production now, minimum viable, scaffolded as a contract for later, or deliberately deferred rather than quietly dropped.

This is a deeply hands-on role for an architect who has delivered AI platforms and solutions at enterprise scale – someone who knows why a technique works and when it survives contact with enterprise data, latency budgets, unit economics, and governance, and who still builds prototypes and reference implementations. Our environment is Azure-primary with Databricks.

Responsibilities

Architecture Ownership and Direction

• Own the enterprise AI reference architecture end to end, establishing scalable standards, principles, and design patterns across business groups.
• Own the roadmap for agentic systems platforms, translating business objectives into sequenced technical strategy, milestones, and a build-depth decision for every use case with explicit, traceable reasoning.
• Design and maintain the paved road for AI development – golden patterns for building, serving, evaluating, and integrating models and agents, with an API-first integration default, MCP by exception against explicit criteria, and guardrails inherited rather than bolted on per team – so teams ship without re-solving solved problems.
• Guide optimization of AI compute and token usage as adoption scales, and lead build-versus-buy, model, and framework decisions with evidence – benchmarks, cost per answer, real-load latency.

Semantic Search, Retrieval and RAG Architecture

• Chunking, embedding and indexing: design chunking strategy per content type and tune size, overlap, and boundaries against measured retrieval quality; select and evaluate embedding models; own re-embedding and versioning.
• Query construction and hybrid retrieval: implement query rewriting, decomposition, and expansion; combine dense and sparse retrieval with fusion and re-ranking; manage the recall-versus-precision and latency trade-off explicitly.
• Retrieval and response evaluation: instrument recall, precision and faithfulness against curated golden datasets; ground answers in the certified semantic layer and knowledge graphs where they outperform vector retrieval; design for graceful abstention over confident wrong answers.
• Define prompt and instruction management standards – versioning, templating, context budgeting, and regression testing on prompt or model changes.
• Own the AI gateway and multi-model routing, matching each request to the right model on…