Describe what went wrong, whether it was data issues, wrong assumptions, deployment challenges, or business pressure. Explain how you identified the problem, what you changed, and how that experience shaped the way you approach deep learning work today. Focus on practical lessons rather than technical perfection.

AI models aren’t static. What works perfectly today can drift tomorrow as user behavior, market conditions, or data sources evolve.

Continuous retraining, monitoring, and feedback loops are critical but each comes with its own challenges.

How do you approach model maintenance in dynamic environments? Do you rely on automated drift detection, human-in-the-loop reviews, or a mix of both?
Share your strategies and experiences , what’s worked best for you in keeping AI performance aligned with reality?

 AI reviewing applications make the process faster, fairer, and purely skill-based. 

Remove bias by focusing on actual performance, not just resumes.

It gives data professionals a real chance to stand out.

Trusting AI in hiring is a step toward smarter, merit-driven careers.