Principal Engineer - Digital AI, Consumer TIC

What matters most

• Turn AI requirements into assurance services

  The posting centers on translating AI governance, model risk, regulations, standards and customer needs into practical evaluation methods, controls, certification programs and scalable services. Prepare an example that traces a requirement through to an assessment method and a customer-facing deliverable.

• Evaluate AI risks and control gaps

  The role calls for assessing performance, transparency, reliability, human oversight, fairness, privacy, security and operational resilience. Prepare to explain how you would identify a failure mode, test it, assess its controls and communicate the remaining risk.

• Lead across emerging AI technologies

  The advertised scope spans predictive and generative AI, foundation models, RAG, agents, multi-agent systems and AI lifecycle assurance. Prepare to show how evaluation methods would change across these systems rather than treating them as one category.

• Provide global technical leadership

  The position is described as a global subject matter expert and calls for technical writing, presentations, thought leadership and stakeholder engagement. Prepare examples of explaining technical judgments to both technical and nontechnical audiences.

Likely challenges to think through

• Making assurance repeatable without oversimplifying

  A likely challenge, inferred from the mandate for scalable offerings, is balancing consistent assessment criteria against differences among AI use cases. Prepare to describe which tests and evidence could be standardized and where context-specific judgment remains necessary.

• Testing rapidly changing systems

  The posting names model drift, hallucinations, prompt injection, jailbreaks and unsafe agent actions. A likely difficulty is that a point-in-time test may not capture behavior after models, prompts or workflows change. Prepare an approach combining scenarios, adversarial testing and monitoring.

• Reconciling competing stakeholder needs

  The role translates regulations, standards and customer needs into services and values influence across varied stakeholders. A plausible challenge is resolving tension between rigorous evidence and a usable assessment. Prepare an example of making such a trade-off explicit without weakening the stated assurance objective.

Topics to prepare

• AI governance and assurance

  Posting: Translate AI governance, model risk management and regulatory requirements into evaluation methods, controls and assurance offerings. Suggested revision: Explain how a governance objective becomes a testable control, required evidence and an assessment decision.

• AI system risks and control gaps

  Posting: Assess performance, transparency, traceability, explainability, reliability, human oversight, fairness, privacy, security, accountability and resilience. Suggested revision: For a hypothetical AI service, identify one failure mode and one control gap in each of three risk areas.

• Model types and lifecycle risks

  Posting: Working knowledge spans predictive AI, LLMs, RAG, foundation models, agents and multi-agent systems. Suggested revision: Compare where hallucinations, data leakage, model drift and unsafe actions could arise across three of these system types.

• Evaluation method selection

  Posting: Methods include benchmarking, scenario-based testing, red teaming, fairness and robustness evaluation, and model monitoring. Suggested revision: Explain which method would best investigate a prompt-injection risk versus a model-drift risk, and what each method cannot establish.

• Standards and conformity assessment

  Posting: The role develops standards and certification services; familiarity with governance frameworks and emerging global regulations is preferred. Suggested revision: Outline the difference between a technical requirement, an assessment method and evidence for a certification decision without assuming a particular framework is mandated.

Skills to revise

• Build a risk-to-evidence matrix

  Posting: The role turns governance and model-risk requirements into practical controls and assurance frameworks. Suggested exercise: Draft a one-page matrix linking five AI risks to controls, evaluation methods, evidence and residual gaps.

• Design an AI evaluation plan

  Posting: Benchmarking, scenario-based testing, fairness and robustness testing are named methods. Suggested exercise: Specify test cases, comparison criteria, pass/fail rationale and limitations for a hypothetical AI system.

• Probe generative and agentic failure modes

  Posting: Prompt injection, jailbreaks, data leakage, unsafe actions and unreliable outputs are named risks. Suggested exercise: Write adversarial scenarios for a hypothetical RAG or agentic workflow, recording the expected safe behavior and evidence needed to assess it.

• Review monitoring and mitigation controls

  Posting: Model monitoring and assessment of risk-mitigation controls are expected. Suggested exercise: Draft a monitoring review that identifies drift indicators, escalation triggers and how to check whether a mitigation remains effective.

• Write and present an assurance proposal

  Posting: Technical writing, presentation and stakeholder engagement support customer-facing services. Suggested exercise: Prepare a short assessment-service brief covering scope, method, evidence, limitations and intended audience, then rehearse a concise explanation for technical and nontechnical stakeholders.

• Map your experience to the qualifications

  Prepare a one-page chronology showing relevant technical work, direct AI experience, education, and examples of governance or model-risk requirements translated into evaluations, controls, assurance frameworks, or services. The posting specifies 15+ years of technical experience and 5+ years working directly with AI.

• Build a senior-level project walkthrough

  Choose one AI assurance or governance project and prepare a concise account of the problem, your decisions, stakeholders, evaluation approach, evidence produced, and outcomes. Distinguish your own contribution from the team’s.

• Practice an AI assessment design exercise

  As interview practice, outline how you would assess a hypothetical RAG or agentic system. Include its intended use, failure modes, controls, test scenarios, and evidence needed to evaluate reliability, human oversight, privacy, and security; this is a preparation exercise, not a stated hiring assignment.

• Prepare a testing and monitoring example

  Bring an example or sample plan covering benchmarks, scenario-based testing, red teaming, fairness or robustness testing, and monitoring. Explain how results would expose risks such as hallucinations, prompt injection, data leakage, drift, or unsafe actions, and how you would assess mitigations.

• Show how technical work becomes an offering

  Draft a short outline for turning an emerging AI risk or requirement into a repeatable assessment, certification, training, or advisory service. The role calls for translating technologies, standards, regulations, and customer needs into scalable offerings.

• Rehearse communication for different stakeholders

  Prepare a brief technical explanation and a plain-language version of the same AI risk finding, including the evidence, uncertainty, and recommended action. The posting emphasizes technical writing, presentations, and engagement with technical and business stakeholders.

• Bring targeted questions for UL Solutions

  Write questions about which AI assurance offerings need development first, how standards and conformity assessment work informs them, what evidence customers expect, and how this principal engineer collaborates with customers, regulators, and internal teams.