Neural Network Architect

We are building a new AI function to modernize how our metals operations generate, connect, and act on data. The Neural Network Architect will lead the design and implementation of a unified data and AI infrastructure that links our Enterprise Resource Planning (ERP), shop floor process/control systems (SCADA, PLC, MES/historian data), Laboratory Information Management Systems (LIMS), cost accounting systems, and Customer Relationship Management (CRM) platforms into a single, coherent architecture.

This is a foundational, cross-functional role. The person in this seat will not just design neural network models, but they will architect the data backbone that makes reliable, production-grade AI possible across the enterprise: from predictive quality control and yield optimization on the shop floor, to demand forecasting that links CRM and production data, to cost-to-serve models that tie financials to real-time operations.

While "Neural Network Architect" is the title, this role is as much about systems integration, data engineering, and information architecture as it is about neural network design. Candidates should be equally comfortable designing a data pipeline between a PLC historian and an ERP as they are designing or evaluating a deep learning model. We are not looking for a pure research scientist, rather we are looking for someone who can turn fragmented industrial data into an infrastructure that AI systems (including neural networks) can reliably run on.

• Architect a scalable, secure data platform that integrates ERP, shop floor/process control systems, LIMS, cost/financial systems, and CRM into a shared data layer.
• Define data models & schemes to link above data with data security and pathways for statistical models to be integrated
• Establish real-time and batch data pipelines connecting operational technology (OT) systems on the shop floor with information technology (IT) systems (ERP, CRM, finance).

• Design, train, and deploy neural network and other ML models on top of the unified data infrastructure for use cases such as predictive maintenance, quality/defect prediction, yield optimization, demand forecasting, and cost anomaly detection.
• Evaluate where deep learning is the right tool versus simpler statistical or rules-based approaches, given industrial data constraints (sparsity, sensor noise, labeling challenges).
• Build MLOps practices for model versioning, monitoring, and retraining as plant and market conditions evolve.

• Partner with IT, OT/automation engineers, lab managers, finance, and sales operations to map data sources, ownership, and access rules across departments.
• Establish data governance standards: data quality, lineage, security, and access control across sensitive systems (cost data, customer data, process parameters).
• Select and implement integration tooling (APIs, middleware, historians, data buses) appropriate for connecting legacy industrial systems with modern cloud/on-prem AI infrastructure.

• Serve as the technical authority on how AI infrastructure should be built as the company's AI department scales.
• Advise leadership on architecture trade-offs, build-vs-buy decisions, and sequencing of integration work (e.g., which systems to connect first for fastest ROI).
• Document architecture decisions and mentor future data engineers/ML engineers hired into the department.

• Bachelor's or Master's degree in Computer Science, Data Engineering, Electrical Engineering, Industrial Engineering, or related field (PhD a plus but not required).
• 7+ years of experience in data architecture, data engineering, or ML/AI engineering, including at least 3 years working with industrial or manufacturing data (OT/IT integration experience strongly preferred).
• Demonstrated experience designing and deploying neural networks or other ML models in production (not just research/prototype settings).
• Hands-on experience integrating disparate enterprise systems a" direct experience with ERP platforms (SAP, Oracle, Infor, etc.), MES/historian systems (e.g., OSIsoft PI, Ignition, Wonderware), and CRM platforms (Salesforce, Dynamics, etc.) is highly valued.
• Strong data engineering skills: SQL, Python, data pipeline tools (Airflow, dbt, Kafka, or similar), and cloud or on-prem data platforms (Azure, AWS, GCP, Snowflake, Databricks).
• Working…