Job description

Chevron is accepting online applications for the position Principal AI Engineer through November 2nd, 2026 at 11:59 p.m. (Central Time). We are 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 role establishes the reference architectures, deployment patterns, model lifecycle controls, safety guardrails and operational readiness criteria required for AI solutions in live industrial environments. This position is part of the OT Artificial Intelligence Team, responsible for providing technical leadership and execution support for the evaluation, architecture and operationalization of AI in OT environments. The role enables OT AI priorities by turning proven AI capabilities into repeatable, secure and supportable designs that can be adopted across business units without creating isolated solutions. Our team is highly technical, creative and cross-functional. The architect will shape the standards that govern AI-enabled operator assistance, computer vision, predictive and prescriptive analytics, intelligent automation, agentic workflows and physical AI across operational environments. Success means accelerating business value while maintaining safety, cybersecurity, reliability, human oversight and clear accountability for AI behavior. This is a dedicated OT AI architecture role. It covers the design, evaluation, deployment and governance of artificial intelligence capabilities used in industrial operations, including AI at the edge, retrieval augmented generation, computer vision, predictive and prescriptive models, generative AI, agentic systems and physical AI. The role works across OT domains only as needed to ensure AI solutions are safe, secure, reliable and supportable. Responsibilities for this position may include but are not limited to: 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. Required Qualifications 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. 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. Preferred Qualifications MS 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. Relocation Options: Relocation will not be considered. International Considerations: Expatriate assignments will not be considered. Chevron regrets that it is unable to sponsor employment Visas or consider individuals on time-limited Visa status for this position. U.S. Regulatory notice: Chevron is an Equal Opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, religious creed, sex (including pregnancy), sexual orientation, gender identity, gender expression, national origin or ancestry, age, mental or physical disability, medical condition, reproductive health decision-making, military or veteran status, political preference, marital status, citizenship, genetic information or other characteristics protected by applicable law. We are committed to providing reasonable accommodations for qualified individuals with disabilities. If you need assistance or an accommodation, please email us at emplymnt chevron.com . Chevron participates in E-Verify in certain locations as required by law.

Requirements

Must-haves

Chevron is accepting online applications for the position Principal AI Engineer through November 2nd, 2026 at 11:59 p.m. (Central Time).
Responsibilities for this position may include but are not limited to:
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.

Nice-to-haves

MS 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.

Application prep

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Keywords to mirror

aiagilemachine learningazurego

Platform: workday
Effort: long

Benefits

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