Machine Learning Engineer resume keywords and examples

Machine learning engineer roles sit between data science and backend engineering. Hiring managers look for one thing above all: models that ran in production and kept running. Research results without deployment read as a data scientist resume.

Since 2024 most postings also ask about large language models — fine-tuning, retrieval-augmented generation, evaluation. If you've shipped an LLM feature, put it near the top.

By Kartikeya Mishra. Reviewed October 2026.

What recruiters search for

Grouped the way machine learning engineer postings usually ask for them.Use the posting's exact spelling, and only list what you've actually used.

Frameworks

PyTorch is the most requested; list what you train with.

Python
PyTorch
TensorFlow
scikit-learn
Hugging Face

Production

This cluster is what makes it an engineering role.

MLOps
Model Deployment
Docker
Kubernetes
AWS SageMaker
Feature Store

LLMs

Name specific techniques, not just "AI".

Large Language Models
Retrieval-Augmented Generation
Fine-Tuning
Prompt Engineering
Vector Databases
Evaluation

Domains

Include only the ones relevant to the posting.

Natural Language Processing
Computer Vision
Recommendation Systems
Deep Learning

Bullets: before and after

Illustrative examples — the numbers are made up to show the shape of a strong bullet. Use your own.

Worked on an LLM chatbot.
Shipped a RAG support assistant on internal docs (pgvector + GPT-4o), with an evaluation set of 600 questions; it now resolves 34% of tickets without an agent.
Why it works: Technique, evaluation and business effect.
Deployed models.
Moved 11 models from notebooks to a SageMaker pipeline with automated retraining and drift alerts, cutting model refresh from quarterly to weekly.
Why it works: Classic MLOps result.
Improved model accuracy.
Raised search ranking NDCG@10 from 0.61 to 0.70 with a two-tower model, lifting click-through 9% in an A/B test.
Why it works: Offline metric plus online validation.

Mistakes we see on machine learning engineer resumes

Only offline metrics: Accuracy on a test set is half the story. Show latency, cost, and the online result.
"AI" everywhere: Specific terms — RAG, fine-tuning, embeddings, evaluation — are what get searched. "AI-powered" isn't a skill.

Job titles to search and mirror

Machine Learning Engineer
ML Engineer
AI Engineer
Applied ML Engineer
MLOps Engineer

Certifications worth listing

AWS Certified Machine Learning – Specialty
Google Professional Machine Learning Engineer
NVIDIA Deep Learning Institute certificates

Only if you hold them. Write the official name in full.

Have a machine learning engineer posting open? Check your resume against it.

Check my resume

Common questions

Should I list LLM projects if they're side projects?

Yes, in a Projects section with the stack and how you evaluated it. Evaluation is what most side projects skip and interviewers ask about.