Principal Engineer, Digital AI

Quick Facts

Principal Engineer role focused on Digital AI; Global Subject Matter Expert for AI governance and AI lifecycle assurance.

Description

Serve as the Global Subject Matter Expert for Digital AI technologies, providing technical leadership to help develop standards, certification, assurance, training, advisory, and assessment services across digital AI systems. Translate emerging technologies, regulations, standards, and customer needs into scalable offerings. Cover AI governance and lifecycle assurance for enterprise and cloud AI, predictive and Generative AI, foundation and frontier models, AI agents and agentic workflows, and multi-agent systems.

Responsibilities

• Provide technical leadership for AI governance and enterprise/cloud AI
• Develop and apply evaluation and assurance approaches for predictive and generative AI systems
• Establish AI lifecycle assurance capabilities, including model lifecycle risk areas
• Identify AI failure modes and control gaps across reliability, transparency, fairness, privacy, security, accountability, and operational resilience
• Use AI evaluation/testing methods such as benchmarking, scenario-based testing, red teaming, and robustness checks
• Support standards and certification-style offerings by converting regulatory and customer requirements into practical controls and methodologies

Requirements

• Bachelor's degree in a related technical field (Computer Science, Software Engineering, AI, Data Science, Computer Engineering, Information Systems, or equivalent)
• 15+ years of technical experience across software/cloud, AI/ML, cybersecurity/technology risk, model lifecycle management, digital assurance, or related domains
• 5+ years of direct experience working with AI technologies
• Demonstrated ability to translate AI governance/model risk and regulatory requirements into practical evaluation methodologies, controls, assurance frameworks, certification programs, or customer-facing services
• Demonstrated ability to evaluate and test AI systems and identify failure modes and control gaps across performance, transparency, traceability, explainability, reliability/robustness, human-in-the-loop, fairness/bias, privacy, security, accountability, and operational resilience
• Working knowledge of AI evaluation and testing methodologies including benchmarking, scenario-based testing, red teaming, bias/fairness evaluation, robustness testing, and model monitoring
• Working knowledge of predictive AI/ML, Generative AI, LLMs, foundation/frontier models, RAG, AI agents, and multi-agent systems, including governance and operational risks (hallucinations, prompt injection, jailbreaks, data leakage, model drift, unsafe actions, output reliability)
• Strong presentation, technical writing, thought leadership, and stakeholder engagement capabilities

Benefits

• Estimated salary range: $155,000–$175,000 (based on skills, experience, and location)
• Annual bonus eligibility with a target payout of 20% of base salary
• Health benefits: medical, dental, vision
• Wellness benefits: mental and financial health
• Retirement savings: 401K
• Paid time off: 15 days vacation; 12 days holiday (including floating holidays); 72 hours sick time