ATS Keywords

AI Engineer ATS Keywords That Actually Get Your CV Seen

Applicant tracking systems filter AI Engineer applications before a human ever sees them, scoring CVs against the specific technical vocabulary in the job description. Missing terms like RAG, vector database, or model fine-tuning can sink a strong candidate even when the underlying experience is there. This guide lists the keywords, phrases, and formatting choices that actually move the needle.

Optimize Your CV

ATS Optimisation

Must-Have Keywords

Include these keywords in your CV to improve your ATS score.

LLM fine-tuning Retrieval-Augmented Generation (RAG) Vector databases (Pinecone, Weaviate, FAISS) Prompt engineering PyTorch TensorFlow Hugging Face Transformers LangChain / LlamaIndex Embeddings and semantic search Model deployment and serving (SageMaker, Vertex AI) MLOps Model quantization and distillation Docker and Kubernetes CI/CD for ML pipelines Python GPU optimization and CUDA Model monitoring and observability A/B testing and offline evaluation

Strengthen Your CV

Power Phrases

Use these multi-word phrases to strengthen your CV.

Fine-tuned a Llama-3 8B model on domain-specific data, improving task accuracy from 71% to 89%

Built a production RAG pipeline serving 50,000+ daily queries with sub-200ms retrieval latency

Reduced LLM inference costs by 35% through quantization and dynamic batching

Deployed and monitored 12 models in production using automated CI/CD and drift-detection pipelines

Designed a vector search architecture handling 10 million+ embeddings at 99.9% uptime

Built a prompt engineering and evaluation framework that cut hallucination rate by 40%

Scaled a fine-tuning pipeline across multi-GPU clusters to train on 500 million+ tokens

Shipped an agentic workflow that automated 60% of a manual customer support process

Avoid These

Words to Avoid

Remove these overused or weak words from your CV.

hard-working team player detail-oriented results-driven responsible for experienced with AI ninja / rockstar / guru passionate about technology

ATS Score

Before & After ATS Optimization

Before

38% F

Missing Keywords

Retrieval-Augmented Generation (RAG) Vector databases LLM fine-tuning MLOps Prompt engineering

After

91% A

Added Keywords

Retrieval-Augmented Generation (RAG) Vector databases (Pinecone, FAISS) LLM fine-tuning MLOps pipelines Prompt engineering

Formatting

ATS-Friendly Format Tips

Use standard section headers like 'Experience' and 'Skills'; ATS parsers often fail to categorize creative headers like 'My Journey' or 'What I Bring'.

Avoid tables, text boxes, and multi-column layouts, since many ATS parsers read them out of order or drop them entirely.

Spell out acronyms at least once, such as 'Retrieval-Augmented Generation (RAG)', so the CV matches both the acronym and full-term searches recruiters run.

Save and submit as a .docx file or a text-based PDF, never a scanned image or a PDF exported from a design tool that flattens text to outlines.

Mirror the exact phrasing from the job posting where truthful; if the listing says 'LLM fine-tuning' and your CV says 'model tuning', the ATS match score can drop significantly.

List technical skills in a dedicated, plain-text 'Skills' section in addition to weaving them into experience bullets, since some parsers weight the skills section more heavily.

Use standard fonts like Arial, Calibri, or Times New Roman, and avoid embedded icons or graphics for contact information, which some parsers cannot read at all.

FAQ

Frequently Asked Questions

Most modern ATS platforms do not hard-reject; they score and rank CVs so recruiters see the highest matches first. A poorly optimized CV usually is not rejected outright, it just gets buried below dozens of better-matched applications and is never opened.

No. List the frameworks and tools you can discuss in depth at interview, and prioritize the ones named in the specific job posting. Padding a skills list with unfamiliar tools risks an easy technical screening failure.

Two to three natural mentions across your summary, skills section, and a relevant experience bullet is enough. Repeating it excessively reads as keyword stuffing to a human reviewer even if the ATS score ticks up slightly.

Yes, ideally. The two titles share a technical core, but the highest-weighted keywords differ: AI Engineer postings skew toward LLMs, RAG, and prompt engineering, while ML Engineer postings often weight classical modeling and feature engineering more heavily.

Yes, as long as you are honest about the context. List it under a 'Projects' section or note '(personal project)' rather than implying professional production experience you do not have; this holds up far better under interview questioning.

No, and it can backfire. Most modern ATS platforms strip formatting and can flag hidden or off-color text as manipulation, and any recruiter who opens the raw text will see it immediately, which damages credibility.

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