Machine Learning Engineer Resume: Projects, Stack and Metrics

Machine learning roles attract crowded applicant pools, and many resumes look the same: a long list of libraries, a Kaggle competition and "built a model with 95% accuracy". What separates candidates is evidence that you can take a model from an idea to something that runs reliably in production and changes a business number.

What ML hiring managers look for

1. Modelling depth: choosing the right approach, evaluating it properly, knowing why it works
2. Engineering: clean code, data pipelines, testing, version control
3. Production: deployment, serving, monitoring, retraining (MLOps)
4. Impact: what changed for users or the business

Your resume should show all four. Many candidates show only the first.

Structure

1. Header with GitHub, Hugging Face or portfolio links
2. Summary: your focus (NLP, computer vision, recommendations, forecasting), core stack and one result
3. Experience
4. Projects: essential if you're early-career or moving from data science or software engineering
5. Skills
6. Education and publications (if relevant)

Keep a single-column, ATS-friendly format. ML teams use applicant tracking systems too.

The skills section

Group skills so a recruiter can scan them against the job description:

• Languages: Python, SQL, C++ (if you use it)
• ML and deep learning: PyTorch, TensorFlow/Keras, scikit-learn, XGBoost, Hugging Face Transformers
• LLMs and NLP: fine-tuning (LoRA), retrieval-augmented generation, embeddings, vector databases, evaluation
• Data: pandas, Spark, Airflow, dbt, feature stores
• MLOps and deployment: Docker, Kubernetes, MLflow, Weights & Biases, ONNX, TensorRT, Triton, FastAPI, CI/CD
• Cloud: AWS SageMaker, GCP Vertex AI, Azure ML

List what you've actually used, and match the names to the posting exactly ("PyTorch", not "Pytorch").

Writing ML bullets: model metric + system metric + business metric

The strongest bullets connect three levels:

• Model: precision, recall, F1, AUC, RMSE, BLEU, accuracy on a named test set
• System: latency, throughput, cost per prediction, model size, uptime
• Business: revenue, conversion, fraud caught, hours saved, tickets reduced

Examples:

• "Built a gradient-boosted fraud model (AUC 0.94 vs 0.88 baseline) and deployed it as a real-time FastAPI service at 15 ms p95, blocking an estimated ₹2 crore in fraudulent transactions in its first quarter."
• "Fine-tuned a 7B-parameter LLM with LoRA for support-ticket routing, raising routing accuracy from 71% to 89% and cutting average resolution time by 18%."
• "Converted the image classifier to ONNX and optimised it with TensorRT, reducing GPU inference cost 60% at the same accuracy."
• "Set up MLflow tracking and an Airflow retraining pipeline with drift monitoring, cutting model refresh time from two weeks to one day."

Use your own numbers. If you can't share exact business figures, use percentages or scale.

Projects that stand out

Strong ML projects go beyond a notebook:

• Deployed: an API or demo someone can try (Hugging Face Spaces, Streamlit, a small web app)
• Evaluated honestly: a clear baseline, a held-out test set, error analysis
• Engineered: tests, a README, reproducible training, containerised
• Real data: messy, real-world data beats a famous clean dataset

For each project: a title, the stack, two or three bullets on the problem, the approach and the result, and links.

Showing MLOps experience

Even small-scale MLOps experience is valuable. Mention:

• Experiment tracking (MLflow, Weights & Biases)
• Model packaging and serving (Docker, FastAPI, Triton, BentoML)
• Pipelines and orchestration (Airflow, Kubeflow, Prefect, ZenML)
• Monitoring for drift and performance in production
• CI/CD for models, and model versioning

Common mistakes

1. Listing 40 libraries with no evidence of using them
2. Accuracy numbers with no baseline or dataset context
3. Only notebook projects, with nothing deployed
4. Research jargon without explaining the outcome
5. Ignoring the job description's own terms

Share your work as a portfolio

ML work is easier to judge when people can see it. A portfolio page that links to your best repositories, demos and write-ups lets a hiring manager check your work in a minute. See how to create a shareable online resume and portfolio.

Preparing for interviews? Read our Python interview questions and the system design interview guide, and see the software engineer resume guide for more on engineering bullets.