Principal Industrial AI Data Architect - US Remote

Hexion Careers • Worthington, OH

5d • Remote

About The Position

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, and industrial data is structured for real-time inference and long-term analytics. This role is the bridge between data, semantics, and AI execution.

Requirements

• Bachelor's degree in Computer Science, Engineering, or related field (Master's preferred)
• 10+ years of experience in data architecture, industrial data systems, or IoT platforms
• Strong experience with time-series data (e.g., historian systems), data pipelines, and ETL/ELT
• Strong experience with distributed data systems
• Understanding of AI/ML data requirements and feature engineering concepts
• Strong system design and data modeling skills
• Ability to connect business, operational, and AI requirements
• High attention to data consistency and integrity
• Cross-functional collaboration

Nice To Haves

• Experience with Industrial IoT or edge-to-cloud platforms
• Experience with Manufacturing systems (OT + IT integration)
• Experience with Cloud data platforms (AWS preferred)
• Familiarity with Streaming architectures
• Familiarity with Event-driven systems
• Familiarity with Data governance frameworks

Responsibilities

• Define Industrial Data Architecture for AI: Design end-to-end data flows from Edge systems to cloud to AI pipelines to edge inference. Define data storage patterns (time-series, relational, event-based) and data movement and transformation strategies. Ensure architecture supports real-time processing, batch analytics, and model lifecycle integration.
• 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.
• Integrate Semantic Model with Data Pipelines: Translate canonical semantic definitions into physical data models, schemas, and pipelines. Ensure all data structures conform to enterprise standards and platform contracts.
• Enable Scalable AI Model Integration: Define data interfaces required by internal AI teams and external model providers. Support model versioning, feature compatibility, and performance validation.
• 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, and reuse of data pipelines across customers and deployments.
• 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 infrastructure), and AI/Data Science teams (model requirements and validation). Ensure consistent execution across domains.

Benefits

• We invest in innovation, sustainability, and continuous development—equipping you with the tools, training, and opportunities to excel.
• With an unwavering commitment to safety, partnership, belonging, and impact, we empower you to lead change and strengthen industries worldwide.

What This Job Offers

• Job Type: Full-time
• Career Level: Principal