🎯 ML, DL, GenAI - What Do You Really Need?

It’s not about who knows the most models. It’s about who can solve the problem with the right approach.

🚀 In interviews and real-world projects, here’s what separates noise from value:

• Can they choose the right approach? → Classical ML, Deep Learning, or GenAI not everything needs the latest hype.
• Do they know when not to use GenAI? → It’s impressive to know LLMs. It’s smarter to know when not to call them.
• Can they debug when pre-built solutions fail? → You don’t need a model zoo, you need people who can trace the issue and fix it.
• Can they explain their trade-offs and iterate with clarity? → Choosing between latency, accuracy, explainability, and cost is real work.

💡 Skip the overly academic or overly abstract interviews. Hire those who think in problem-first, data-smart, solution-aware ways.

✅ Evaluate with real-world scenarios.
✅ Prioritize learning agility and debugging mindset.
✅ Look for clarity in reasoning, not just complexity in vocabulary.

---

#MLvsDLvsGenAI #AIHiring #GenAIRealityCheck #DataDrivenEngineering #AIProductThinking #ProblemFirst #ResponsibleAI #TechRecruiting #DebuggingMatters #RealWorldAI #InterviewWisdom #EnterpriseAI #ThinkBuildLearn

 Keep Thinking!!!