Hirerizz teamAI and ML resumes are crowded with the same buzzwords. What sets one apart is evidence that a model went into production and helped someone. Use the specific terms below for the work you did, and put a measurable result next to each project.
Keywords to put on a AI and ML resume
Use the words that match your real experience. Do not copy a list you cannot back up in an interview.
Languages and libraries
Python, PyTorch, TensorFlow, scikit-learn, Hugging Face, pandas, NumPy, SQL, Spark.
Models and techniques
machine learning, deep learning, NLP, computer vision, recommendation systems, large language models, LLMs, RAG, fine-tuning, embeddings, vector databases, prompt engineering, time series.
Production and MLOps
model deployment, MLOps, MLflow, model monitoring, feature store, inference latency, batch and real-time inference, Docker, Kubernetes, GPU, cost per request.
Evaluation
accuracy, precision and recall, F1 score, AUC, offline evaluation, A/B testing, human evaluation, data labelling, bias checks.
How to write them into your bullets
A keyword on its own is easy to ignore. A keyword inside a result is hard to ignore. Replace the brackets with your own numbers, and only keep a line if it is true.
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Weak: Built machine learning models. Stronger: Built and deployed a [type] model that [does what], improving [metric] from [X] to [X] and serving [X] requests a day.
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Weak: Worked on LLM projects. Stronger: Built a retrieval-augmented (RAG) assistant over [X] documents, with an evaluation set of [X] questions that reached [X]% correct answers.
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Weak: Improved model performance. Stronger: Cut inference latency from [X] ms to [X] ms and cost per request by [X]% by changing the serving setup, with no drop in accuracy.
Mistakes that hold a resume back
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Listing buzzwords for projects that stayed in a notebook. Say clearly what shipped and what stayed experimental.
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Quoting accuracy alone. Name the metric that matters for the task, and the baseline you beat.
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Leaving out the data side. Where the data came from, how you cleaned it and how you checked quality is half the job.
Where to place the keywords
Put your strongest keywords in three places: your headline or summary, your skills section, and the bullets under your most recent role. Use the same spelling the job post uses. If a term has a long form and a short form, write both once, for example "Key Performance Indicators (KPIs)", and use the short form after that. Keep the resume to what is true, and keep it readable for a person as well as for software.
Check your resume against a real job post
Paste the job post and your resume into the resume keyword check to see which of its words your resume already uses.
See open AI and ML jobs: browse jobs.