He built programming language but still got 0 Interviews (Here’s Why) | Fix your AI career Ep 1

Book a session with me here: https://www.schoolwhool.com/fix-your-... it's free Mitansh -   / mitansh-kanani-9a80812b6   Gaurav -   / gaurav2022   Discord community -   / discord   In this session, we review an aspiring AI/Machine Learning engineer's resume and portfolio projects to break down why he is struggling to get shortlisted. We discuss: Why relying blindly on AI tools without understanding the underlying data leads to critical issues (like data leakage and bad baselines). The difference between software engineering projects vs. real-world machine learning workflows. How to properly frame business problem statements, handle class imbalance, and choose the right evaluation metrics (AUC-ROC vs. Accuracy). Practical advice on positioning your resume and reaching out directly to hiring managers. Timestamps: 00:00 - precap 01:05 - Programming language project 06:30 - Credit Risk AI Project 19:15 - Kaggle Chemistry Project 25:05 - Voice Accessibility Project 28:05 - Job hunt challenges 30:50 - My Feedback 36:00 - Real-World ML Pipeline vs. Toy Projects 42:30 - Resume Redesign & Practical Next Steps If you are currently preparing for machine learning roles and facing similar roadblocks, let us know your thoughts or questions in the comments Tags: #machinelearning #datascience #careergrowth #schoolwhool Why Your AI/ML Projects Aren’t Getting You Hired #machinelearning Reviewing an AI/ML Resume & Projects | Why Candidates Get Rejected #datascience AI Career Mentorship Ep 1: Fixing Common Project & Resume Mistakes #machinelearning The Biggest Mistake AI Beginners Make When Building Projects He Built 5 ML Models But Kept Getting Rejected — Here’s Why Why Most Machine Learning Projects Fail in Real Job Interviews