Principal AI Engineer: Governance, Cloud & Generative AI

UL Solutions; Chicago (IL); On-site; USD 155,000 - 175,000; Full time

Benefits offered by this job

Health benefits

Annual bonus eligibility

Paid time off

Job summary

UL Solutions is seeking a Principal Engineer - Digital AI to act as the Global Subject Matter Expert for Digital AI technologies, driving governance, risk assessment, and assurance across enterprise and cloud AI systems.

In this role you will translate evolving regulations, standards, and customer needs into scalable UL Solutions offerings, lead evaluation methodologies, and guide teams through model risk, bias, explainability, and lifecycle governance.

Qualifications

• Bachelor's degree in a technical field; advanced degree preferred.
• 15+ years of technical experience across software platforms, cloud AI, cybersecurity, or related domains.
• 5+ years working with AI technologies.
• Ability to translate AI governance and regulatory requirements into evaluation methodologies.
• Experience evaluating AI systems for performance, bias, privacy, and safety.

Responsibilities

• Provide technical leadership for AI governance and lifecycle assurance.
• Translate regulations and customer needs into scalable UL Solutions offerings.
• Evaluate AI systems and identify risks, failure modes, and control gaps.
• Develop and communicate evaluation methodologies and assurance frameworks.
• Engage with stakeholders and present findings clearly.

Skills

AI governance

Stakeholder engagement

Technical writing

Presentation skills

Model risk management

Regulatory knowledge

Education

Bachelor's degree

Advanced degree

Job description

UL Solutions is seeking a Principal Engineer - Digital AI to act as the Global Subject Matter Expert for Digital AI technologies, driving governance, risk assessment, and assurance across enterprise and cloud AI systems.

In this role you will translate evolving regulations, standards, and customer needs into scalable UL Solutions offerings, lead evaluation methodologies, and guide teams through model risk, bias, explainability, and lifecycle governance.