Lead AI Architect

Domino's•Ann Arbor, MI

About The Position

The Lead AI Architect owns the technical architecture for Domino’s enterprise AI platform across classical AI, generative AI, and agentic AI. As the senior individual-contributor technical authority for AI, this role sets direction for how capabilities are built, served, orchestrated, and governed, and drives outcomes through design authority and influence rather than direct reports. The role owns the AI reference architecture, the paved road for delivery teams, and build-depth decisions for incoming use cases. This is a hands-on architecture role requiring experience delivering AI platforms and solutions at enterprise scale. The architect applies technical judgment to enterprise data, latency, unit economics, and governance, while building prototypes and reference implementations. Domino’s environment is Azure-primary with Databricks. The role also establishes platform standards, evaluation and governance practices, security patterns, operational observability, and reusable implementation patterns, while partnering across engineering, architecture, data governance, security, finance, business teams, and technology vendors.

Requirements

Deep enterprise architecture and systems-design expertise, including end-to-end AI platforms spanning applications, models, data, APIs, integration, identity, infrastructure, and operations.
Strong grounding in distributed systems, event-driven and service-oriented patterns, resilience, high availability, fault tolerance, and disciplined architectural decision-making.
Deep understanding of classical machine learning, deep learning, NLP, foundation models, transformers, embeddings, semantic retrieval, RAG, fine-tuning, multimodal AI, and agentic or multi-agent architectures, including their trade-offs and failure modes.
Ability to design structured and unstructured data ingestion and transformation, chunking and indexing, vector and hybrid retrieval, knowledge graphs, metadata models, lineage, provenance, data quality, and access controls.
Ability to ground conversational experiences in structured data, including text-to-SQL and multi-turn dialogue.
Experience establishing evaluation frameworks and benchmarks, automated testing, release controls, model and prompt versioning, observability, quality monitoring, incident response, rollback, and continuous improvement using MLOps and LLMOps principles.
Ability to build identity, authorization, privacy, model security, human oversight, regulatory controls, and auditability into architecture from the start.
Understanding of AI-specific threats including prompt injection, sensitive-data leakage, unsafe agent actions, and model or software supply-chain risk.
Ability to make quantitative architecture decisions about latency, throughput, concurrency, availability, capacity, inference architecture, compute utilization, quality-versus-performance trade-offs, consumption forecasting, and cost optimization, including AI FinOps at scale.
Ability to translate business strategy into qualified AI use cases, measurable outcomes, target-state architectures, investment priorities, and delivery roadmaps, and connect solutions to operational or financial KPIs.
Demonstrated ability to deliver from discovery and architecture through experimentation, production deployment, and operational improvement.
Ability to build or review prototypes using Python, SQL, and Spark; validate assumptions; review implementation designs; diagnose cross-layer failures; and defend technical trade-offs.
Production depth in DevSecOps, Infrastructure as Code, Kubernetes, and model serving.
Experience defining reusable patterns and standards, leading architecture reviews, mentoring engineers and architects, and aligning product, data science, engineering, infrastructure, security, and business teams.
Ability to create clarity in ambiguity, lead with integrity and humility, and communicate effectively with executives and practitioners.
A strong record of leading complex AI or ML solutions in production at enterprise scale, typically 10+ years across software, data, or ML engineering with significant architecture leadership.

Nice To Haves

Hands-on experience with Microsoft technologies including Azure AI Foundry and Agent Service, Azure OpenAI, Azure ML, AI Search, API Management, Functions, Databricks, and Copilot Studio.
Experience with agent frameworks such as LangGraph or Semantic Kernel and MCP.
Experience in retail, restaurants, e-commerce, or supply chain, including high-volume, real-time workloads, streaming technologies such as Kafka or Event Hubs, and model risk management.
Experience with production multi-agent systems and agent runtimes, policy-aware agent gateways with authenticated tool routing and server registries, and GPU-accelerated serving stacks.
Contributions to open-source AI projects, published work, or recognized community involvement.

Responsibilities

Own the enterprise AI reference architecture and establish scalable standards, principles, and design patterns across business groups.
Own the agentic systems platform roadmap, translate business objectives into sequenced technical strategy and milestones, and document build-depth decisions for each use case.
Design and maintain reusable AI development patterns for building, serving, evaluating, and integrating models and agents.
Guide AI compute and token optimization; lead evidence-based build-versus-buy, model, and framework decisions using benchmarks, cost, latency, and real-load performance.
Design chunking, embedding, indexing, query construction, hybrid retrieval, re-ranking, and versioning strategies for semantic search and RAG.
Evaluate retrieval and responses using curated datasets and measures such as recall, precision, and faithfulness; ground answers in certified semantic layers and knowledge graphs where appropriate, and design graceful abstention behavior.
Define prompt and instruction management standards, including versioning, templating, context budgeting, and regression testing.
Own the AI gateway and multi-model routing, establish model benchmarking and selection practices, and define when prompt engineering, fine-tuning, customization, or caching is appropriate.
Establish evaluation harnesses as launch gates, including offline and online evaluation, calibrated LLM-as-judge, A/B and shadow testing, and quality thresholds.
Architect agent orchestration across agents, tools, retrieval, and human review; define domain-agent scopes, reusable skills and tools, memory and context management, and tool/function-calling and MCP contracts.
Establish agent guardrails, authorization and permission models, approval and escalation paths, sandboxing, fallback and safe-failure behavior, tracing, regression testing, audit, and incident response.
Define the agent registry, tool catalog, memory and state services, and lifecycle for moving agents from prototype to monitored production; apply agentic patterns in engineering practice.
Define responsible AI standards and align them with enterprise data governance policy.
Establish AI security patterns for prompt injection, data exfiltration, jailbreaks, and model or supply-chain risk; partner with Data Governance on data classification, stewardship, certified sources, and access controls.
Classify use cases by risk, drive attestations with Security, Legal, Privacy, and Risk, and lead technical reviews of third-party models and vendors.
Instrument tracing, telemetry, and drift signals; establish cost attribution and cost per successful answer as a key metric, and partner with platform and finance teams to connect investment to measurable outcomes.
Define the Azure-native platform stack and lifecycle with MLOps and Engineering, including AI services, registries, CI/CD, landing zones, environments, identity, networking, capacity, and LLMOps/AgentOps practices.
Partner with Engineering and Architecture on data readiness, certified metrics, lineage, data contracts, and translating reference patterns into production services and sanctioned builder environments.
Provide technical leadership through design reviews, system-level reasoning, mentorship, and cross-team decisions without direct reporting relationships.
Communicate architecture trade-offs to senior technology leaders and peer architects, and lead decisions with partners such as Microsoft, Databricks, AI vendors, and systems integrators.
Write architecture decision records, reference patterns, and reusable accelerators; develop proof-of-concept reference implementations that help teams bring AI solutions from concept to production.