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Explain PM collaboration and AI code quality

Lead BankSoftware Engineer · Onsite
MediumSoftware EngineerOnsiteBehavioral & Leadership

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This question assesses the candidate's ability to balance rapid product delivery with long-term code maintainability, specifically when integrating AI-generated code or working under aggressive PM timelines.

The Scenario

Imagine you are working on a high-priority feature for a financial product. Your Product Manager (PM) is under significant pressure to ship the feature by the end of the quarter to meet a market deadline. To speed up development, the PM suggests that the team heavily utilize AI coding assistants to generate boilerplate and logic. However, you notice that the AI is generating code that is syntactically correct but lacks context on your bank's strict security protocols and complex legacy state management, leading to subtle bugs.

The Prompt

How do you approach the conversation with your PM regarding these quality risks without stalling the project? Describe a situation where you had to advocate for engineering standards despite pressure to prioritize speed.

Your answer should address:

  1. How you identify and quantify 'technical debt' versus 'feature progress' to a non-technical stakeholder.
  2. How you ensure the AI-assisted code undergoes rigorous validation, such as code reviews or automated testing, without creating a bottleneck.
  3. How you balance being a 'team player' with being a 'custodian of system integrity.'

Hints

Hint 1

Focus on framing the risk in terms of business impact, such as potential outages, security audits, or future maintenance costs, rather than just 'code cleanliness.'

Hint 2

Think about proposing a 'middle ground' approach, such as AI-assisted drafting paired with mandatory human oversight or enhanced automated regression suites.

Hint 3

Highlight a specific past experience where you had to negotiate scope or timeline to ensure architectural health.

Follow-up questions

  • How would you train or mentor junior developers who might be over-relying on AI and skipping the fundamental understanding of the logic they are submitting?
  • If the PM insisted that the business risk of missing the deadline outweighs the technical risk of the bugs, how would you document this decision to protect the team?
  • What specific metrics or 'quality gates' would you put in place to ensure that AI-generated code doesn't degrade the system over time?