Principal Applied AI Architect
Sets company-wide architecture and strategy for data and applied AI, connecting governed data foundations to production intelligence and measurable business outcomes. The role requires 14+ years of experience, strong production engineering judgment, executive partnership, and hands-on delivery.
About the job
Responsibilities
- Define target-state architecture for the data estate and applied AI, aligned with business priorities.
- Design how warehouses, lakes, pipelines, streaming, integrations, model serving, retrieval, embeddings, prompt and model management, guardrails, and evaluation fit together.
- Establish source-of-truth models and a shared semantic business model for a reliable, governed platform.
- Set naming, modeling, lineage, integration, security, privacy, compliance, access-control, retention, and audit standards.
- Own AI strategy, build-versus-buy decisions, sequencing, platform modernization investments, evaluation, rollout, monitoring, model risk, and responsible AI standards.
- Advise data, product, and engineering leadership on system capabilities, costs, risks, and expected returns.
- Turn strategy into actionable projects, contribute to planning, direct human and agentic workstreams, and implement production systems.
- Mentor Applied AI Scientists and other individual contributors and establish reusable engineering patterns and decision records.
- Raise observability, resilience, reliability, maintainability, and operational standards for AI systems.
- Advance accuracy at scale, adaptive procurement, predictive ordering, and agentic copilots for procurement workflows.
Requirements
- At least 14 years of experience in applied data science, machine learning, data architecture, or production AI systems.
- Track record of shipping work that moved a company-level metric.
- Experience serving as a technical counterpart to product, strategy, or business leadership.
- Deep knowledge of production systems and the businesses they serve.
- Ability to work across data architecture, applied AI, statistical modeling, model and agent evaluation, and production engineering.
- Experience with security, privacy, compliance, reliability, cost, and performance constraints.
- Ability to communicate technical tradeoffs and build investment cases for leadership.
Nice to Have
- Experience with procurement, finance, catalog, pricing, availability, vendor, or spend data.
- Experience with agentic AI, retrieval systems, embeddings, predictive models, and responsible AI programs.
- Experience mentoring senior technical contributors and coordinating cross-functional delivery.
Skills
Applied Ai, Machine Learning, Data Architecture, Data Science, Data Warehousing, Data Lakes, Data Pipelines, Streaming, Model Serving, Retrieval, Embeddings, Observability, Responsible Ai, Statistical Modeling, Production Engineering
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