I Scraped 14,000 Job Descriptions. These 37 AI Keywords Get 3X More Interviews.

The setup: Spent September analyzing every tech job posting on Indeed, LinkedIn, and direct company career pages that mentioned AI skills.

The insight: Your resume is probably using dead keywords from 2023.

The problem: Terms like "Machine Learning" and "Deep Learning" dropped 12% and 9% in employer demand. Why? Everyone says they know ML. Nobody proves they can apply it.

What works instead: Hyper-specific, application-focused terms that show you've touched real AI tools in production.

Here's the data.

The 37 Keywords Ranked by ATS Performance

Tier 1: The Interview Magnets (89% callback correlation)

These appeared in 89% of resumes that got interviews vs 31% of rejections:

1. Generative AI (+317% YoY)

• Why it works: Generic enough for ATS, specific enough to signal current skills
• Where to use: Professional summary, skills section, project descriptions
• Example: "Deployed generative AI pipeline reducing content creation time 67%"

2. LLM Fine-Tuning (+289% YoY)

• Why it works: Screams "I don't just use ChatGPT, I train models"
• Where to use: Technical projects, model optimization achievements
• Example: "Fine-tuned LLaMA 2 model on domain-specific data, improving accuracy from 73% to 91%"

3. Prompt Engineering (+274% YoY)

• Why it works: Bridge between technical and non-technical roles
• Where to use: Literally anywhere you've used AI tools strategically
• Example: "Prompt engineering for customer service automation (CSAT: 4.2→4.7)"

4. AI Ethics & Compliance (+261% YoY)

• Why it works: Companies are terrified of AI lawsuits
• Where to use: Even if you're an engineer—shows mature judgment
• Example: "Implemented AI ethics review process preventing 3 potential bias incidents"

5. Multimodal AI (+253% YoY)

• Why it works: Future-proof term; text-only AI already feels dated
• Where to use: Projects involving image, video, audio + text
• Example: "Built multimodal search combining text queries with image recognition"

Tier 2: The Differentiators (73% callback correlation)

Still strong, but not quite the magnets:

6-15 (The Framework Gang):

• LangChain (+241%)
• Hugging Face Transformers (+229%)
• RAG Architecture (+218%) ← Retrieval-Augmented Generation
• Vector Databases (+207%)
• Embeddings (+198%)
• OpenAI API (+192%)
• Claude API (+187%)
• Model Deployment (+181%)
• AI Model Monitoring (+176%)
• Reinforcement Learning from Human Feedback (RLHF) (+169%)

Why these work: They prove you've shipped AI products, not just taken courses.

Tier 3: The Table Stakes (52% callback correlation)

Baseline terms—won't hurt you, won't save you:

16-25:

• Natural Language Processing (NLP) (+43%)
• Computer Vision (+38%)
• Neural Networks (+29%)
• TensorFlow (+21%)
• PyTorch (+19%)
• Python for AI (+17%)
• Data Preprocessing (+14%)
• Model Training (+11%)
• AI/ML Pipelines (+8%)
• Cloud AI Services (+6%)

Tier 4: The Specialists (Use ONLY if true expertise)

26-37 (Niche but powerful):

• AI Safety Research (+157%)
• Constitutional AI (+143%)
• Adversarial ML (+128%)
• Federated Learning (+119%)
• AutoML (+107%)
• Explainable AI (XAI) (+94%)
• Edge AI Deployment (+88%)
• Quantization & Pruning (+81%)
• Few-Shot Learning (+76%)
• Transfer Learning (+68%)
• AI Governance Frameworks (+61%)
• AI Product Management (+54%)

The Shocking Declines

Term 2024 Usage 2025 Usage % Change

Machine Learning 87% 75% -12% Deep Learning 71% 62% -9% Artificial Intelligence 93% 89% -4% Big Data 54% 41% -13%

Why the drop?

These terms are now assumed. Saying "I know Machine Learning" in 2025 is like saying "I know Microsoft Office" in 2015.

Employers want proof you can:

1. Use specific AI tools in production
2. Solve real problems with AI (not just train models)
3. Navigate ethical/compliance minefields

How to Actually Use This Data

Strategy 1: The Keyword Sandwich

❌ Bad: "Experienced in AI and machine learning"

✅ Good: "Deployed generative AI solutions using LangChain and RAG architecture, reducing customer support costs 34% while maintaining AI ethics compliance"

The formula: [Tier 1 keyword] + [Tier 2 framework] + [Business impact] + [Tier 1 keyword]

Strategy 2: The Proof Point Method

For every AI keyword, attach a metric or specific tool:

• ❌ "Skilled in prompt engineering"

• ✅ "Prompt engineering with Claude API: reduced API costs $47K→$12K monthly"

• ❌ "Familiar with LLM fine-tuning"

• ✅ "Fine-tuned Mistral 7B on 50K customer transcripts (F1 score: 0.67→0.89)"

Strategy 3: The Non-Technical Pivot

If you're NOT a ML engineer but use AI tools:

Focus on Tier 1 terms + business outcomes:

"Marketing Manager leveraging prompt engineering and generative AI to scale content production 5x while maintaining brand voice consistency (measured via sentiment analysis: 0.82 alignment score)"

This works because:

• "Prompt engineering" = technical credibility
• "Generative AI" = current skills
• "5x" = business impact
• "Sentiment analysis" = you measure things

The ATS Reality Check

What I learned scraping these systems:

1. ATS systems weight recent terms higher

• A keyword from 2023 job descriptions scores 0.4x
• Same keyword from 2025 descriptions scores 1.0x
• This explains the "Machine Learning" drop

2. Exact match matters more than you think

• "LLM fine-tuning" ≠ "Fine-tuning LLMs"
• Use the exact phrase from job descriptions
• CV by JD auto-matches job description phrasing

3. Context windows are small

• ATS reads ~15 words around each keyword
• Cluster related terms together
• Don't scatter AI keywords randomly

The Free Tool That Does This For You

Yeah, you could manually insert all 37 keywords.

Or you could paste your resume + target job description into CV-by-JD and let it:

1. Identify missing Tier 1 keywords from the job post
2. Suggest natural placements in your existing bullets
3. Ensure ATS-friendly exact phrase matching
4. Score your keyword density vs. successful resumes

It's free. Built it after doing this analysis and realizing manual keyword optimization is insane.

The Data Sources

• 14,247 job postings (Jan-Sep 2025)
• Companies: FAANG, startups (Series A-D), Fortune 500
• Roles: Data Scientist, ML Engineer, AI Product Manager, AI-adjacent marketing/ops
• Analysis method: TF-IDF + manual validation + callback correlation
• Callback data: From 2,847 job seekers who shared outcomes

Bottom Line

Stop saying "Machine Learning."

Start saying:

• "Generative AI"
• "LLM fine-tuning"
• "Prompt engineering"
• "RAG architecture"
• "AI ethics compliance"

The difference?

Generic terms = you took a course

Specific terms = you shipped a product

ATS systems know the difference. So do hiring managers.

Want the full 37-keyword checklist + ATS optimization guide? just paste your resume into CV by JD and let it do the work in 30 seconds.