Principal Industrial AI Data Architect - US Remote

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Position Overview The Principal Industrial AI Data Architect is responsible for designing and governing the data architecture that enables reliable, scalable AI across industrial environments. This role ensures that:
Data pipelines are aligned with the canonical semantic model Features used in AI models are consistent across training and runtime Industrial data is structured for real-time inference and long-term analytics This role is the bridge between data, semantics, and AI execution.

Job Responsibilities
1. Define Industrial Data Architecture for AI Design end-to-end data flows from:
Edge systems cloud AI pipelines edge inference Define:
Data storage patterns (time-series, relational, event-based) Data movement and transformation strategies Ensure architecture supports:
Real-time processing Batch analytics Model lifecycle integration
2. Design Feature Pipelines and Delivery for AI Models Design and govern the pipelines, storage, and lifecycle that build and deliver features to AI models, based on canonical definitions established by the Principal Manufacturing & Semantic Architect. Define feature engineering pipelines for both training (cloud) and inference (edge) environments Ensure consistency between training datasets and runtime inference data Prevent feature drift and data mismatch through automated validation
3. Integrate Semantic Model with Data Pipelines Translate canonical semantic definitions into:
Physical data models Schemas Pipelines Ensure all data structures conform to:
Enterprise standards Platform contracts Additional

Job Responsibilities
4. Enable Scalable AI Model Integration Define data interfaces required by:
Internal AI teams External model providers Support:
Model versioning Feature compatibility Performance validation
5. Design for Multi-Tenant and Product Use Cases Ensure data pipelines and access patterns support multi-tenant environments, including:
Customer data isolation and secure access controls Scalable onboarding of new tenants and use cases Reuse of data pipelines across customers and deployments

Note:

The underlying data model for multi-tenancy is governed by the Principal Manufacturing & Semantic Architect.
6. Collaborate Across Teams Partner with:
Principal Manufacturing & Semantic Architect (canonical model definition and feature semantics) Principal Edge & OT Architect (edge data ingestion and inference data requirements) Platform Engineering (implementation and…