Machine Learning Engineer keywords for resume and LinkedIn
ML engineering searches now split between classic and generative work. One recruiter queries pytorch, mlops, and model deployment; another queries llm, fine-tuning, and rag. Profiles that cover both vocabularies surface in both pipelines. Framework names matter because they are filterable, and production terms like inference optimization and model monitoring separate engineers who ship from people who train notebooks. Use the exact names recruiters type: pytorch, hugging face, sagemaker.
Tools and platforms (12)
- python
- pytorch
- tensorflow
- scikit-learn
- hugging face
- mlflow
- kubernetes
- docker
- sagemaker
- spark
- langchain
- weights & biases
Hard skills (12)
- model training
- model deployment
- mlops
- feature engineering
- llm fine-tuning
- retrieval augmented generation
- model monitoring
- distributed training
- prompt engineering
- inference optimization
- a/b testing
- vector databases
Soft skills (6)
- problem solving
- communication
- collaboration
- research mindset
- pragmatism
- adaptability
Where these belong
Choose headline terms by target: mlops and pytorch for platform roles, llm and rag for generative ones, both if you straddle. List every framework in the Skills section and describe one production system in About with latency or cost numbers. The Job Description Keyword Finder reveals which vocabulary a team uses, and Resume Keyword Match keeps your resume aligned to it.
Honesty rule: add a term only if it is true of you. Keyword coverage opens doors; interviews walk through them.
Make it yours: Resume Keyword Match
Lists orient, postings decide. Paste your resume and a real job description to see your true coverage and gaps in one pass.
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