ATS Keywords

Machine Learning Engineer ATS Keywords That Get Your CV Past the Screener

Machine Learning Engineer roles routinely draw hundreds of applicants per opening, and most companies now run every CV through an ATS before a human ever sees it. These systems scan for exact-match tool names, frameworks, and technique keywords pulled straight from the job posting, so a CV that only says 'built AI models' can lose to a weaker candidate whose CV says 'PyTorch, MLOps, model deployment.'

Optimize Your CV

ATS Optimisation

Must-Have Keywords

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

PyTorch TensorFlow Model deployment MLOps Feature engineering LLM fine-tuning Vector databases A/B testing Docker Kubernetes CI/CD pipelines Model monitoring Scikit-learn Hyperparameter tuning SQL Distributed training Retrieval-Augmented Generation (RAG) MLflow

Strengthen Your CV

Power Phrases

Use these multi-word phrases to strengthen your CV.

Reduced model inference latency by 42% through quantization and ONNX runtime optimization, enabling real-time serving at a fraction of the compute cost.

Deployed and monitored 15+ production ML models serving 2M+ daily predictions with automated drift detection and retraining pipelines.

Fine-tuned open-source LLMs on domain-specific data, improving task accuracy from 68% to 91% while cutting inference cost by 35%.

Built an end-to-end MLOps pipeline, including feature store, CI/CD, and model registry, that cut deployment time from three weeks to under two days.

Designed and ran A/B tests across 20+ model variants, driving a 12% lift in conversion directly attributable to the ML recommendation engine.

Architected a real-time feature engineering pipeline processing 500K events per second with sub-100ms latency for fraud detection.

Led migration of batch inference to a Kubernetes-based serving layer, improving uptime from 97% to 99.95%.

Implemented a RAG-based retrieval system that reduced hallucination rate by 60% in a production customer-support LLM application.

Avoid These

Words to Avoid

Remove these overused or weak words from your CV.

hardworking team player detail-oriented responsible for passionate about results-driven synergy think outside the box

ATS Score

Before & After ATS Optimization

Before

42% D

Missing Keywords

MLOps model deployment PyTorch feature engineering vector databases

After

91% A

Added Keywords

MLOps PyTorch model deployment feature engineering LLM fine-tuning vector databases

Formatting

ATS-Friendly Format Tips

Use standard section headers like 'Experience', 'Skills', and 'Education'; ATS parsers often fail to categorize content placed under creative headers like 'My Journey'.

Save your CV as a .docx or a text-selectable PDF, never an image-based or design-heavy PDF exported from a graphic design tool, since many ATS engines cannot extract text from a graphic layout.

Avoid tables, text boxes, and multi-column layouts for your skills and experience; some ATS parsers read columns left to right across the page and scramble the content.

List your tech stack as a plain, comma-separated 'Technical Skills' line, for example Python, PyTorch, TensorFlow, Kubernetes, MLflow, SQL, so exact-match keyword scanners can find them without parsing prose.

Mirror the exact terminology from the job description; write 'machine learning engineer' and 'MLOps' if that is what the posting uses, rather than only synonyms like 'AI engineer' or 'ML ops'.

Spell out acronyms at least once, for example 'Retrieval-Augmented Generation (RAG)', so both acronym-searching and full-term-searching ATS configurations catch the match.

Keep skills and tools out of headers, footers, and icon-based skill-bar graphics; many ATS systems skip headers and footers entirely and cannot read text baked into images.

FAQ

Frequently Asked Questions

Yes, where possible. ATS keyword matching is largely literal, so if a posting says 'MLOps' use that exact term rather than only writing 'machine learning operations' or 'model deployment automation'.

Two to three natural mentions, such as in the skills list, a bullet point, and the summary, is enough. Repeating the same keyword five or six times reads as spam to both ATS relevance scoring and human recruiters.

No. List the frameworks and tools you can actually discuss in an interview and that match the job description; irrelevant keyword-stuffing can trigger a lower quality score in modern semantic ATS tools and will fail you at interview stage regardless.

No, most ATS keyword matching treats them as distinct exact-match strings, so include both if you have real experience with both rather than assuming one implies the other.

Most ATS systems don't parse external links for keyword content, but including one still matters for the human reviewer who reads your CV after it clears the initial filter.

Only if the job posting explicitly lists them as requirements. For technical ML roles, ATS and recruiters weight tool and technique keywords far more heavily than generic soft-skill terms.

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