Principal Industrial AI Data Architect - US Remote

Hexion
Columbus, United States of America
12 days ago

Role details

Contract type
Permanent contract
Employment type
Full-time (> 32 hours)
Working hours
Regular working hours
Languages
English
Experience level
Senior

Job location

Remote
Columbus, United States of America

Tech stack

Artificial Intelligence
Customer Data Management
Data Architecture
Data Governance
ETL
Data Structures
Data Systems
Distributed Data Store
Cloud Services
Data Streaming
Systems Integration
Data Server Interface
Data Storage Technologies
Performance Testing
Real Time Systems
Feature Engineering
Data Ingestion
Data Strategy
Event Driven Architecture
Information Technology
Machine Learning Operations
Physical Data Models
Multiaccess Edge Computing
Software Version Control
Data Pipelines

Job description

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., 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
  1. 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
  1. 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

  1. Enable Scalable AI Model Integration

Define data interfaces required by:

  • Internal AI teams
  • External model providers

Support:

  • Model versioning
  • Feature compatibility
  • Performance validation
  1. 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.

  1. 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)
  • AI/Data Science teams (model requirements and validation)

Ensure consistent execution across domains.

Requirements

  • 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

Minimum Qualifications

  • 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, Experience with:

  • Industrial IoT or edge-to-cloud platforms

  • Manufacturing systems (OT + IT integration)

  • Cloud data platforms (AWS preferred)

Familiarity with:

  • Streaming architectures
  • Event-driven systems
  • Data governance frameworks

Other

Leadership Expectations

Operate as a thought leader in industrial data architecture and AI data strategy

Influence without direct authority across multiple teams and partners

Drive standards adoption for data pipelines and AI data practices across internal and external stakeholders

Balance long-term architectural vision with near-term delivery needs

About the company

Imagine Everything. Build the Future with Hexion. At Hexion, we push boundaries, rethink possibilities, and create real impact. We activate science to deliver progress-developing breakthrough solutions that strengthen industries, protect communities, and drive a more sustainable future. This is where bold thinkers, problem-solvers, and innovators come together to shape what's next. Whether you're engineering advanced materials, transforming manufacturing technologies, or leading strategic innovation, your ideas and actions leave a lasting mark. We cultivate an inclusive culture of growth, collaboration, and accountability, ensuring every contribution propels us forward. We don't follow the status quo-we challenge it, disrupt it, and improve it. Every role at Hexion is part of something bigger. 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.

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