Principal OT AI Solution Architect

Chevron • Houston, TX

3d

About The Position

Chevron is seeking an OT Solutions Architect specializing in artificial intelligence to define how AI capabilities move safely and consistently from concept and field trial into supported operational use. This dedicated OT AI architecture role establishes reference architectures, deployment patterns, model lifecycle controls, safety guardrails, and operational readiness criteria for AI solutions in live industrial environments. The position is part of the OT Artificial Intelligence Team and provides technical leadership and execution support for evaluating, architecting, and operationalizing AI in OT environments. It turns proven capabilities into repeatable, secure, and supportable designs that can be adopted across business units.

The architect shapes standards for AI-enabled operator assistance, computer vision, predictive and prescriptive analytics, intelligent automation, agentic workflows, and physical AI. The scope includes the design, evaluation, deployment, and governance of AI capabilities such as edge AI, retrieval augmented generation, generative AI, and agentic systems. Success requires accelerating business value while maintaining safety, cybersecurity, reliability, human oversight, and clear accountability for AI behavior. The role collaborates with OT Architecture, OT Cyber, operations, engineering, data science, Facility of the Future, and business unit teams, and contributes to an embedded Agile team. Relocation and expatriate assignments will not be considered; Chevron is unable to sponsor employment visas or consider candidates on time-limited visa status.

Requirements

• Bachelor of Science degree in Engineering, Computer Science, Data Science, or a related field, or equivalent experience.
• Eight 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.
• Understanding of AI data requirements, including provenance, quality, labeling, contextualization, privacy, retention, and secure movement.
• Working knowledge of Microsoft Azure AI and machine learning services and hybrid or on-premises deployment patterns.
• Ability to communicate AI design decisions, alternatives, risks, and consequences to engineers, data scientists, cyber reviewers, operators, and business sponsors.
• Demonstrated ability to reach a defensible technical verdict on AI vendors, platforms, and models by distinguishing claims from verified capability.

Nice To Haves

• Master of Science degree in Engineering, Computer Science, Data Science, or a related field.
• Experience architecting generative AI, agentic AI, or multimodal systems for operational environments.
• Experience with computer vision, physical AI, robotics, drones, or autonomous systems in industrial settings.
• Experience with MLOps platforms and model lifecycle automation across cloud, on-premises, and edge environments.
• Experience with responsible AI practices, model risk management, explainability, bias testing, and assurance evidence.
• Experience creating curated, labeled, or synthetic industrial datasets for training and evaluation.
• Familiarity with industrial semantic models, knowledge graphs, digital twins, or asset context used to ground AI solutions.
• Experience with edge AI accelerators, containerized inference, and orchestration approaches for industrial environments.
• Experience designing AI observability, performance monitoring, and incident response processes.
• Upstream, midstream, or downstream domain experience and comfort working directly with asset and operations personnel.
• Experience operating within a stage-gated AI evaluation process and publishing a no-go decision as readily as a go decision.

Responsibilities

• Author and shepherd approved OT AI reference architectures and reusable design patterns through OT Architecture and cybersecurity review.
• Define guardrails for deploying and operating AI models and agents in industrial environments, including human oversight, fail-safe behavior, degraded modes, rollback, explainability, and auditability.
• Establish architecture patterns for edge AI, retrieval augmented generation, computer vision, predictive and prescriptive analytics, generative AI, agentic systems, and physical AI.
• Own OT AI model lifecycle architecture, including model intake, validation, versioning, deployment, monitoring, drift detection, retraining triggers, retirement, and rollback.
• Define requirements for AI training, validation, and inference data, including provenance, quality, labeling, contextualization, access controls, and permitted movement across security environments.
• Provide the architecture of record for OT AI capabilities entering evaluation, pilot, and field deployment so parallel efforts converge on governed, reusable designs.
• Evaluate AI vendors, platforms, and models for operational fit, safety, cybersecurity, supportability, interoperability, scalability, and lock-in exposure.
• Design test and validation approaches that reflect real asset conditions, including disconnected operation, limited compute, variable data quality, and operational constraints.
• Produce AI risk assessments, architecture decision records, model documentation, data flow diagrams, and operational readiness evidence required for approval.
• Apply Purdue model and ISA/IEC 62443 zone and conduit reasoning to AI connectivity, edge inference, remote support, and model or data movement designs.
• Define observability requirements for AI services, including model performance, confidence, drift, anomalous behavior, resource consumption, and operational impact.
• Partner with OT Architecture, OT Cyber, operations, engineering, data science, Facility of the Future, and business unit teams to operationalize OT AI responsibly.
• Maintain the OT AI architecture roadmap and contribute actively within an embedded Agile team.

What This Job Offers

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