I’m not here to coddle you. Most candidates are delusional about their readiness. If you’re getting rejected repeatedly, it’s likely because you fall into several of these categories. Here’s why you’re failing, and the ruthless fixes.

  1. Shallow, Buzzword-Driven Knowledge
    You throw around terms like “transformers,” “LLMs,” “RAG,” or “fine-tuning” but crumble when asked to explain attention mechanisms from scratch or debug a training loop. Recruiters spot this instantly— you’ve consumed YouTube tutorials and prompted ChatGPT but lack real understanding. In 2026, “prompt engineer” is a joke title; companies want builders.

  2. Fix: Stop skimming. Implement foundational papers from scratch (e.g., build a transformer in PyTorch without copying code). Read “Deep Learning” by Goodfellow cover-to-cover. If you can’t derive backpropagation on a whiteboard, you’re not qualified.
  3. No Meaningful Projects or Portfolio
    Your GitHub is empty, has half-finished Kaggle notebooks, or copies Hugging Face demos. Hiring managers don’t care about your “personal project” that’s a fine-tuned MNIST classifier. They want impact: scalable systems, production deployment, or original contributions.
    Fix: Build and deploy 3-5 substantial projects that solve real problems (e.g., a multi-modal RAG system with evaluation metrics). Open-source them, write detailed blogs, get stars/forks. If your portfolio doesn’t impress a senior engineer in 5 minutes, delete it and start over.
  4. Weak Coding Fundamentals
    You can’t solve medium/hard LeetCode problems efficiently, write clean production code, or design systems. AI roles still require strong software engineering—many “AI experts” fail basic coding rounds because they rely on Copilot too much.
    Fix: Grind 500+ LeetCode problems (focus on hard). Practice system design (use Grokking the System Design Interview). Write code daily without AI assistance. If you’re not in the top 10% of coding speed/accuracy, you won’t pass screens at FAANG or serious startups.
  5. Obvious AI-Generated Applications
    Your resume and cover letter sound robotic, generic, or have hallucinations (wrong company name, impossible claims). ATS and humans now flag AI-written content—it’s an instant reject because it signals laziness and lack of genuine interest.
    Fix: Write everything yourself. Tailor brutally to the job description with specific, quantifiable achievements. Have a human (preferably a hired resume expert) review it. If it reads like ChatGPT output, burn it.
  6. Overclaiming or Lying About Experience
    You list “led LLM development” when you just prompted Claude for a weekend project. Background checks and interviews expose this fast—references don’t match, you can’t discuss details. Trust is everything; one lie tanks you.
    Fix: Be ruthlessly honest. If you’re junior, own it and emphasize potential through projects. Build real experience via internships, freelancing, or contributions. Faking it in AI is suicide because the field moves too fast for impostors.
  7. Failing to Demonstrate Up-to-Date Knowledge
    You’re still talking about TensorFlow while the industry has moved to PyTorch/JAX, or you don’t know the latest (Grok-4, o1 reasoning models, agentic workflows). The field evolves quarterly; outdated candidates get binned.
    Fix: Read arXiv daily, follow key researchers (Karpathy, Sutskever, etc.), implement new papers weekly. Subscribe to premium tools and experiment. If your last big learning was GPT-3, you’re already obsolete.
  8. Poor Interview Communication and Explanation Skills
    You can code but can’t explain your thought process, defend design choices, or teach concepts clearly. AI interviews are conversations—many candidates mumble, ramble, or freeze.
    Fix: Do 50+ mock interviews (Pramp, interviewing.io, or hire a coach). Record yourself explaining concepts like diffusion models. If you can’t teach it simply, you don’t know it.
  9. No Network or Referrals
    You’re cold-applying to 1000 jobs on LinkedIn and getting ghosted. In AI, referrals bypass screens—most good roles fill internally or via networks. Lone wolves die.
    Fix: Network aggressively. Attend conferences (NeurIPS, ICML), post insights on X/LinkedIn, DM recruiters/engineers, contribute to open source for visibility. If you have zero connections in AI, you’re invisible.
  10. Lacking Business Impact or Production Experience
    You have academic/theoretical knowledge but no proof of deploying models that drove revenue, reduced costs, or handled scale. Companies don’t hire science projects; they hire impact.
    Fix: Get production experience—intern, freelance, or join a startup. Quantify everything (“reduced inference cost 40% for 1M daily users”). If your resume has no metrics, it’s trash.
  11. Delusional Expectations for Junior/Entry-Level Roles
    AI automated many junior tasks, so companies hire mid/senior only or freeze headcount. New grads/bootcampers flood the market thinking a certificate equals a $200k job. Reality: entry-level AI roles are rare and hyper-competitive.
    Fix: Accept you may need to start in general software engineering to build skills, contribute unpaid to gain cred, or pivot to related fields (data engineering, MLOps). Lower salary expectations initially. If you’re not exceptional, the market doesn’t owe you an AI job.

Bottom line: The top 1% get hired because they’re obsessively skilled, proven, and relentless. Most applicants are mediocre in a saturated field. If this list stings, good—that means you’re aware. Fix these brutally and consistently for 6-12 months, or accept you’ll stay rejected. The market doesn’t care about your potential; it rewards proven excellence.