[Hiring] OT Artificial Intelligence Test and Assurance Engineer @S3 Int

Role Description

We are seeking an OT Artificial Intelligence Test and Assurance Engineer to independently verify that AI-enabled solutions are safe, secure, reliable, and fit for use in industrial operations. This role develops and executes risk-based assurance strategies across models, data, applications, integrations, edge platforms, and operational workflows before solutions enter service and throughout their production lifecycle.

The position provides independent testing, verification, validation, and assurance support for industrial AI use cases. The engineer establishes reusable test frameworks, evidence requirements, acceptance criteria, and release gates for machine learning, computer vision, generative AI, agentic systems, and AI-enabled applications deployed across edge, on-premises, and authorized cloud environments.

The successful candidate will collaborate with various specialists while maintaining appropriate independence from solution delivery. The focus is on producing objective evidence that AI solutions perform as intended, fail safely, withstand foreseeable misuse, and remain dependable under real-world operating conditions.

This role focuses on test and assurance engineering for industrial AI solutions. Responsibilities include:

• Assurance planning
• Requirements traceability
• Data and model validation
• System and integration testing
• Safety and cybersecurity verification
• Human-factors evaluation
• Performance and resilience testing
• Operational acceptance
• Release evidence
• Post-deployment assurance

The engineer evaluates complete AI-enabled systems—not just model accuracy—under normal, degraded, abnormal, and recovery conditions.

Qualifications

• Bachelor's degree in Computer Science, Software Engineering, Systems Engineering, Electrical Engineering, or a related field, or equivalent practical experience.
• Five or more years of experience in software, systems, automation, or quality engineering, including production verification and validation responsibilities.
• Experience developing risk-based test strategies, acceptance criteria, test cases, traceability, and objective release evidence for complex systems.
• Experience testing machine learning, computer vision, analytics, or other data-driven systems using appropriate performance and error metrics.
• Experience evaluating datasets for quality, representativeness, leakage, imbalance, labeling accuracy, lineage, and version control.
• Experience with automated test frameworks, Python, APIs, data validation, source control, CI/CD, and defect-management tools.
• Experience with system, integration, performance, resilience, cybersecurity, and user-acceptance testing.
• Understanding of AI limitations, uncertainty, drift, model monitoring, human oversight, and lifecycle controls.
• Understanding of industrial cybersecurity, network segmentation, constrained connectivity, high availability, and controlled change in industrial environments.
• Ability to communicate test results, uncertainty, defects, and residual risk clearly to engineers, operators, security reviewers, and decision-makers.
• Demonstrated independence, professional skepticism, and willingness to recommend against release when the available evidence does not support approval.

Requirements

• Master's degree in Systems Engineering, Computer Science, Artificial Intelligence, Safety Engineering, or a related field (preferred).
• Experience assuring generative AI, retrieval-augmented generation (RAG), agentic systems, or multimodal AI, including grounding, hallucination, tool-use, and prompt-injection testing (preferred).
• Experience validating industrial computer vision across variations in lighting, weather, motion, occlusion, viewpoint, background, and equipment state (preferred).
• Experience with model robustness, explainability, fairness, adversarial testing, red teaming, or responsible AI assessment (preferred).
• Experience with safety cases, assurance cases, hazard analysis, failure modes and effects analysis (FMEA), bow-tie analysis, or similar risk-assessment methods (preferred).
• Experience with IEC 61508, IEC 61511, IEC 62443, the NIST AI Risk Management Framework, or other relevant assurance standards and guidance (preferred).
• Experience with simulation, digital twins, hardware-in-the-loop, software-in-the-loop, or synthetic-data testing environments (preferred).
• Experience integrating automated assurance checks into Azure Machine Learning, Microsoft Foundry, Azure DevOps, GitHub Actions, or equivalent platforms (preferred).
• Experience supporting incident investigations, model-performance reviews, and production requalification after material changes (preferred).
• Experience in upstream, midstream, or downstream industrial environments and the ability to work directly with asset and operations personnel (preferred).

Benefits

• Competitive salary
• Health, dental, and vision insurance
• Retirement savings plan
• Professional development opportunities
• Flexible work arrangements

Company Description

We are a leader in industrial AI solutions, dedicated to enhancing safety, efficiency, and reliability in operations.