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ATS Resume Checker for AI Engineers
AI engineer is a new title, and postings for it change vocabulary every few months. Recruiters cope by searching the ATS for exact terms: LLM, RAG, fine-tuning, evals. Our free checker scores your resume against the actual posting, shows which of its terms you're missing, and flags weak bullets. Fittingly, the AI model doing the matching runs in your own browser: nothing is uploaded, and there's no sign-up.
How ATS screening works for AI engineers
Most AI engineer roles are about building products on top of large language models: retrieval, prompts, tool use, evaluation, latency and cost. That is different from training models, and the postings use different words. If your resume says “NLP pipeline” where the posting says “RAG,” an ATS keyword search won't connect them, even though it may be the same work.
The field is full of demos, so hiring managers look for evidence that something reached production. Users served, latency, cost per request, evaluation scores before and after, hallucination rate reduced. A bullet with one of those numbers beats a list of every framework you have tried.
Keywords that matter on an AI engineer resume
Use the exact wording below where it’s true of you. An ATS matches words literally, not by meaning.
| Keyword | Why it matters |
|---|---|
| LLMs / large language models | Write both the acronym and the full phrase; recruiters search either |
| RAG / retrieval-augmented generation | The most common architecture term in current postings |
| Prompt engineering | Still searched literally, even for senior roles |
| Fine-tuning / LoRA | Separates people who adapt models from people who only call them |
| Evals / evaluation | The keyword that signals production maturity |
| Python / TypeScript | Python is assumed; TypeScript matters for product-facing teams |
| Vector databases / embeddings | Name the ones you used: pgvector, Pinecone, Weaviate |
| Agents / tool use / function calling | Fast-growing requirement; use the posting's exact wording |
| Claude / OpenAI / open-weight models | Provider and model names are easy literal matches |
| Latency / cost optimization | Shows you ran something at scale, not only in a notebook |
Three fixes that raise an AI engineer resume’s score
- Say what shipped and who used it. “Built a RAG chatbot” becomes “Shipped a RAG support assistant answering 30% of 12k monthly tickets, with groundedness scored on a 400-question eval set.”
- Spell every term both ways once. “Retrieval-augmented generation (RAG),” “large language models (LLMs).” You match the search whichever form the recruiter types.
- Show evaluation, not only building. One bullet about how you measured quality, caught regressions or cut hallucinations tells a hiring manager you have done this for real.
Questions
How is an AI engineer resume different from a machine learning engineer resume?
AI engineer postings emphasise building with existing models: RAG, prompts, agents, evals, product integration. ML engineer postings emphasise training and serving your own: PyTorch, feature pipelines, MLOps. Many people have done both, so lead with whichever the posting asks for.
Should I list side projects and hackathon demos?
Yes, if your paid AI experience is short, which is true for most people in a field this new. Give each project one line with a link, what it does, and a number: users, stars, eval score. Skip tutorials you followed.
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See your score
Free, no sign-up, and your resume never leaves your device. The checker opens with a typical AI engineer posting loaded, so you see keyword match straight away.