Data · Resume guide
AI engineer postings (LLM-era) screen for applied GenAI vocabulary — RAG, embeddings, prompt engineering, agent frameworks — plus evidence you shipped AI features with evaluation rigor rather than demo-level prototypes.
These terms appear repeatedly in real ai engineer job descriptions. Use the exact forms below where your experience honestly supports them — exact matches rank; paraphrases often don't.
Share of 174 live machine learning engineer postings in our 15 August 2026 snapshot (AI Engineer titles are counted with Machine Learning Engineer in this sample) . Front-load the ones your target job description actually names.
| Skill | Share of postings |
|---|---|
| Machine Learning | 82% |
| LLM | 60% |
| Python | 59% |
| PyTorch | 37% |
| Deep Learning | 27% |
| TensorFlow | 26% |
| NLP | 20% |
| SQL | 20% |
| AWS | 18% |
| Spark | 18% |
| GCP | 15% |
| CI/CD | 14% |
Public ATS boards (Arbeitnow, Greenhouse, Ashby, Google Careers, Lever, RemoteOK, Remotive). Not LinkedIn or Indeed. Snapshot, not a forecast — always mirror the posting in front of you.
Strong ai engineer bullets pair the role's vocabulary with a measured outcome. Use these as patterns — with your real numbers, never invented ones.
“Shipped a RAG-based support assistant (embeddings + hybrid search, custom eval suite) that deflected 38% of tickets at 92% answer accuracy”
“Cut LLM spend 55% via prompt compression, response caching, and routing between model tiers based on query complexity”
Paste the ai engineer job description you're targeting and extract its required skills and repeated phrases.
Rephrase your real experience with the posting's exact terminology — same work, their words.
Reorder your summary, skills, and top bullets so this role's priorities lead.
Verify: check your match score and remaining keyword gaps before submitting.
Use current GenAI terms precisely (RAG, embeddings, evals, agents) and pair each with production evidence — accuracy measured, cost managed, users served. The eval framework you built is often the strongest differentiator on 2026 AI postings.
The highest-weight terms for ai engineer postings currently include: Machine Learning, LLM, Python, PyTorch, Deep Learning, TensorFlow. The definitive list is always the specific job description — paste it into a free keyword check to see exactly which terms your resume shows and which it's missing.
Extract the job description's required skills and repeated phrases, then rephrase your real experience using those exact terms — lead your summary and top bullets with what that posting emphasizes. Never add skills you don't have; keep genuine gaps visible and address them honestly.
Upload your resume, paste the job description, and get your free ATS match score with every missing keyword — then a tailored, ATS-safe rewrite without fabrication.