Principal OT AI Solution Architect

What you'll do

• Author and shepherd approved OT AI reference architectures and reusable design patterns through cybersecurity reviews.
• Define safety guardrails for industrial AI, including human oversight, fail-safe behavior, and explainability.
• Own the OT AI model lifecycle architecture, including intake, validation, monitoring, drift detection, and retirement.
• Define requirements for AI training and inference data, including provenance, quality, and security controls.
• Evaluate AI vendors and platforms for operational fit, cybersecurity, scalability, and lock-in exposure.
• Design test and validation approaches that reflect real asset conditions and operational constraints.
• Produce AI risk assessments, architecture decision records, and data flow diagrams for approval.
• Apply Purdue model and ISA/IEC 62443 standards to AI connectivity and edge inference designs.

What we're looking for

• BS in Engineering, Computer Science, Data Science or a related field, or equivalent experience.
• 8 or more years of experience in operational technology, industrial automation or industrial AI, including significant responsibility for solution architecture.
• Demonstrated experience architecting, deploying or governing AI and machine learning solutions in operational or safety-adjacent environments.
• Experience with model lifecycle management, including validation, versioning, monitoring, drift detection, retraining, rollback and retirement.
• Experience defining AI safety guardrails, human-in-the-loop controls, fallback behavior and operational acceptance criteria.
• Experience designing edge AI solutions for constrained, intermittently connected or disconnected industrial environments.
• Working knowledge of OT cybersecurity, the Purdue reference model and ISA/IEC 62443 zone and conduit concepts as applied to AI systems.
• Working knowledge of Microsoft Azure AI and machine learning services and hybrid or on-premises deployment patterns.
• MS in Engineering, Computer Science, Data Science or a related field (preferred).
• Experience architecting generative AI, agentic AI or multimodal systems for operational environments (preferred).
• Experience with computer vision, physical AI, robotics, drones or autonomous systems in industrial settings (preferred).
• Experience with MLOps platforms and model lifecycle automation across cloud, on-premises and edge environments (preferred).
• Experience with responsible AI practices, model risk management, explainability, bias testing and assurance evidence (preferred).
• Experience creating curated, labeled or synthetic industrial datasets for training and evaluation (preferred).
• Familiarity with industrial semantic models, knowledge graphs, digital twins or asset context used to ground AI solutions (preferred).
• Experience with edge AI accelerators, containerized inference and orchestration approaches for industrial environments (preferred).
• Experience designing AI observability, performance monitoring and incident response processes (preferred).
• Upstream, midstream or downstream domain experience and comfort working directly with asset and operations personnel (preferred).
• Experience operating within a stage-gated AI evaluation process and publishing a no-go decision as readily as a go decision (preferred).