Lead Data Architect

As Lead AI Architect you own the technical architecture for Dominos 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.

Generative AI Portfolio

• 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 complexity cost latency and quality and own the benchmarking discipline for how models are compared selected and re-tested as the landscape shifts.
• Establish the evaluation harness as a launch gate offline/online evaluation LLM-as-judge with human calibration A/B and shadow testing and an explicit quality threshold we wont launch below.
• Hold a clear evidence-based position on when prompt engineering suffices versus when fine-tuning or customization is warranted and define caching strategy as a deliberate cost and latency lever.

Agentic AI Portfolio

• Architect the orchestration layer that chains agents tools retrieval and human review orchestration as the enforced contract domain agents scoped by journey and business context and a reusable skills and tool layer where reusability actually lives.
• Design memory and context management with an explicit position on what persists for how long and under what access rules; define enterprise standards for tool/function calling and MCP server contracts.
• Establish agent guardrails authorization scope permission models approval and escalation paths sandboxing fallback behavior and safe failure plus step-level tracing regression testing audit and incident response.
• Define the agent registry tool catalog memory and state services and lifecycle from prototype to monitored production asset and apply agentic patterns spec-driven development AI-assisted code and test generation to our own engineering practice.

Governance Trust and Security

• Define responsible AI standards bias/fairness testing transparency explainability and human oversight on high-risk outputs aligned with enterprise data governance policy rather than parallel to it.
• Establish AI security patterns for prompt injection data exfiltration jailbreaks and model/supply-chain risk; partner with the Data Governance team on classification stewardship certified sources and access controls Microsoft Purview Entra ID Unity Catalog row-level security PII handling and retention.
• Classify use cases by risk drive attestation with Security Legal Privacy and Risk and lead third-party model/vendor technical review translating findings into architecture constraints.

Observability Economics and Platform Engineering

• Instrument tracing telemetry and drift signals from day one so agent behavior is observable before its questioned; build cost attribution and drive cost per successful answer as a first-class metric.
• Contribute to the instrumentation that ties AI investment to measurable outcomes partnering with platform and finance teams so unit economics are a first-class design constraint.
• Define the Azure-native platform stack and lifecycle with MLOps and Engineering Azure AI Foundry Azure ML AKS model/agent registry CI/CD landing-zone and environment strategy Entra ID and network patterns capacity and LLMOps/AgentOps for versioning promotion and rollback.
• Partner with Engineering and Architecture teams on data readiness certified metrics lineage and data contracts and with Engineering on turning reference patterns into shipped services defining the sanctioned builder environment so teams move fast without bypassing controls.

Technical Leadership

• Raise the bar across Engineering and solution architecture through design reviews system-level reasoning and mentorship driving rigor without direct reporting relationships.
• Translate architecture trade-offs into clear decision-oriented narratives for senior technology leaders and peer architects and lead cross-team decisions with partners such as Microsoft Databricks strategic AI vendors and systems integrators.
• Write architecture decision records reference patterns and reusable accelerators and develop proof-of-concept reference implementations that help teams move AI solutions from concept through production without rebuilding the controls.

Qualifications :

• Deep enterprise architecture and systems-design expertise. Proven ability to design 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.
• Strong foundations in AI machine learning and generative AI. Deep understanding of classical ML deep learning NLP foundation models transformers embeddings semantic retrieval RAG fine-tuning multimodal AI and agentic or multi-agent architectures including when to use each approach how to weigh the trade-offs and where each one can fail.
• Data retrieval and knowledge architecture expertise. Ability to design structured and unstructured data ingestion transformation pipelines chunking and indexing strategies vector and hybrid retrieval knowledge graphs metadata models lineage provenance data quality and access controls including grounding conversational experiences in structured data (text-to-SQL multi-turn dialogue).
• Model evaluation and production lifecycle engineering. Experience establishing evaluation frameworks and benchmarks ( NDCG groundedness LLM-as-judge) automated testing release controls model and prompt versioning observability quality monitoring incident response rollback and ongoing improvement using MLOps and LLMOps principles.
• Security privacy responsible AI and governance. Ability to build identity authorization privacy model security human oversight regulatory controls and auditability into the architecture from the start. Must understand AI-specific threats such as prompt injection sensitive-data leakage unsafe agent actions and model or software supply-chain risk.
• Scalability reliability performance and AI economics. Ability to make quantitative architecture decisions on latency throughput concurrency availability capacity inference architecture compute utilization quality-versus-performance trade-offs consumption forecasting and cost optimization including AI FinOps at scale.
• Business and value-oriented solution architecture. Ability to translate business strategy into qualified AI use cases measurable outcomes target-state architectures investment priorities and delivery roadmaps. Must separate value-producing opportunities from technology demos and tie solutions to operational or financial KPIs.
• End-to-end delivery with hands-on technical credibility. Demonstrated ability to move from discovery and architecture through experimentation production deployment and operational improvement. Able to build or review prototypes (Python SQL Spark) validate assumptions review implementation designs diagnose cross-layer failures and defend technical trade-offs with production depth in DevSecOps Infrastructure as Code Kubernetes and model serving.
• Technical leadership and stakeholder influence. Experience defining reusable patterns and standards leading architecture reviews mentoring engineers and architects and aligning product data science engineering infrastructure security and business teams. Creates clarity in ambiguity leads with integrity and humility and communicates well with executives and practitioners alike.
• Substantial enterprise AI delivery experience. 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.

Preferred Qualifications

• Hands-on experience with Microsoft environment: Azure (AI Foundry and Agent Service Azure OpenAI Azure ML AI Search API Management Functions) Databricks and Copilot Studio; agent frameworks such as LangGraph or Semantic Kernel and MCP.
• Experience in retail restaurant e-commerce or supply chain with high-volume real-time workloads and streaming (Kafka Event Hubs) and model risk management.
• 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.

Additional Information :

Benefits:

• Paid Holidays and Vacation 
• Medical Dental & Vision benefits that start on the first day of employment
• No-cost mental health support for employee and dependents
• Childcare tuition discounts
• No-cost fitness nutrition and wellness programs 
• Fertility benefits
• Adoption assistance
• 401k matching contributions 
• 15% off the purchase price of stock 
• Company bonus 

All your information will be kept confidential according to EEO guidelines.

Remote Work :

No

Employment Type :

Full-time