Technical practice
How would you turn agent traces, prompts, tool calls, and security events into explainable findings?
What to coverThe Agentic AI Forensics workstream calls for structured findings that security analysts can use. Suggested answer focus: describe how you would link evidence to a finding, use prompting, RAG, and structured extraction where useful, and check for unsupported conclusions.
How would you classify cloud resources to discover AI agents and model endpoints at scale?
What to coverThe Cloud Discovery workstream includes identifying AI-enabled workloads and inferring their purpose and security risk. Suggested answer focus: discuss available resource signals, classification uncertainty, and when to use rules, ML, or LLM-based classification.
What failure modes would you examine in a multi-step agent that calls tools?
What to coverThe posting names agent architectures, tool calling, MCP or similar protocols, and common failure modes. Suggested answer focus: trace decisions across steps and distinguish erroneous planning, unsafe tool use, and unreliable tool outputs.
How would you evaluate an LLM-powered security finding or cloud classification pipeline?
What to coverThe role calls for ground-truth datasets, accuracy targets, human review, and regression suites. Suggested answer focus: explain labeling criteria, metrics for consequential errors, and tests that catch regressions across different cases.
How would you detect and investigate declining model quality after deployment?
What to coverMonitoring production quality and drift, then addressing degradation, is an explicit responsibility. Suggested answer focus: compare current outputs with baselines and reviewed examples, investigate changes in inputs or dependencies, and decide when to revise or roll back an approach.
How would you move a Python LLM/ML prototype into a production Go service?
What to coverThe posting pairs Python experimentation and data pipelines with Go production services. Suggested answer focus: explain how you would preserve evaluated behavior while addressing service reliability, API boundaries, testing, and operational trade-offs.
How would you deploy and operate one of these services in a cloud-native environment?
What to coverThe requirements name Docker, Kubernetes, and experience with AWS, GCP, or Azure. Suggested answer focus: discuss packaging, configuration, scaling, and observability choices without assuming a particular cloud provider or architecture.
How would you translate a security researcher's attack pattern into detection and summarization logic?
What to coverThe role explicitly involves working with security researchers on attack patterns and risk signals. Suggested answer focus: clarify the evidence and desired analyst conclusion, then test whether the resulting detection and summary remain accurate on ambiguous cases.
How would you integrate AI-generated results into event, storage, and UI layers?
What to coverThe posting calls for surfacing actionable results to security teams through those layers. Suggested answer focus: describe the result fields, evidence links, uncertainty, and failure handling needed to make a finding usable and reviewable.
How would you use and validate an AI coding assistant during development?
What to coverThe posting requires AI-assisted development and says candidates will be asked about their real-world use and validation of it. Suggested answer focus: distinguish suitable implementation, test, or debugging tasks from work needing closer review, and explain how you verify generated output.
Behavioural practice
Tell me about a time you owned a technical workstream with limited day-to-day direction.
What to coverThe role emphasizes independent ownership in a remote-first, distributed team. Suggested answer focus: use situation, action, and result to show how you set priorities, communicated decisions, and resolved a blocker.
Describe a time you worked with a domain expert to turn an ambiguous signal into a usable feature.
What to coverThe posting describes collaboration with security researchers on detection and summarization logic. Suggested answer focus: give the situation, your actions to clarify the signal and test interpretations, and the resulting change to the feature or shared understanding.
Tell me about a time you found that a shipped ML or LLM feature was underperforming.
What to coverProduction quality monitoring and addressing degradation are stated responsibilities. Suggested answer focus: describe the situation, how you investigated and communicated the issue, what action you took, and the measured result.
Describe a time you had to choose between a promising prototype and a more dependable production approach.
What to coverThe role spans rapid Python experimentation and reliable Go production services. Suggested answer focus: explain the situation, the trade-offs and evidence behind your decision, your actions, and the outcome.
Tell me about a time you made a complex technical result understandable to its users.
What to coverThe forensics workstream aims to deliver explainable, actionable findings for security analysts. Suggested answer focus: use situation, action, and result to show how you handled evidence, uncertainty, and user feedback without overstating the result.