Machine Learning Engineer resume example
Machine learning engineering resumes are judged on one question: did your models make it into production and change something? This example shows how to put deployment, scale, monitoring and business impact ahead of a long list of frameworks.
Called a CV in the UK, Ireland, India, Africa, Australia and many other places, and a resume in North America. The layout below works for both.
Arjun Mehta
Machine Learning Engineer | Recommendation and Ranking Systems | Python, PyTorch, MLOps
Bengaluru, India arjun.mehta@example.com
Summary
Machine learning engineer with five years building and running ranking, recommendation and forecasting models in production. Comfortable across the full lifecycle, from data pipelines and experiments to deployment, monitoring and retraining. Recently focused on retrieval and LLM-based features with careful evaluation.
Skills
- Python
- PyTorch
- scikit-learn
- SQL
- Feature engineering
- Model deployment (Docker, Kubernetes)
- MLflow
- Airflow
- AWS SageMaker
- A/B testing
- Retrieval-augmented generation (RAG)
- LLM evaluation
Experience
Machine Learning Engineer
Apr 2022 to Present
Kirana Cart, Bengaluru, India
- Rebuilt the product ranking model for search, lifting add-to-cart rate by 6% in a two-week A/B test across 3 million sessions.
- Moved 9 models from notebooks to a shared training and serving pipeline with MLflow and Kubernetes, cutting release time from weeks to days.
- Set up drift monitoring and automatic retraining alerts, catching two data quality issues before they reached customers.
- Built a retrieval-augmented help assistant for store partners, with an evaluation set of 400 real questions used to test every change.
Data Scientist
Jul 2020 to Mar 2022
Ledgerline Finance, Pune, India
- Developed a demand forecasting model for cash withdrawals across 1,200 ATMs, reducing cash-out incidents by 14%.
- Wrote feature pipelines in SQL and Python that cut model training data preparation from 2 days to 3 hours.
- Presented model results monthly to operations leaders, translating error metrics into staffing and cost decisions.
Education
B.Tech Computer Science and EngineeringDeccan Institute of Technology, Hyderabad, India
2020
Certifications
- AWS Certified Machine Learning Specialty
Languages
English (fluent) · Hindi (native) · Kannada (conversational)
This example is fictional: the person, employers and figures are invented to show good structure. Use it as a guide and write your own, true details.
What makes this resume work
- 1The headline names the type of ML problem (ranking, recommendation), not just 'AI', which matches how ML job ads are written.
- 2Bullets prove production impact with an A/B test result, not offline accuracy alone.
- 3MLOps work (pipelines, monitoring, retraining) is shown explicitly, because many teams care more about reliability than novelty.
- 4The LLM bullet includes an evaluation set, which signals responsible, testable use of generative AI.
- 5Communication with non-technical leaders is shown, a common differentiator for senior ML roles.
Common mistakes to avoid
- Listing every framework and model type you have touched instead of the ones you used in real projects.
- Reporting only offline metrics like accuracy without saying what changed for users or the business.
- Describing Kaggle or course projects as production experience. Label them honestly as projects.
Applying for a machine learning engineer job?
Pair your resume with a short, specific cover letter. See the machine learning engineer cover letter example, or draft your own with the cover letter generator.
Questions people ask
What is the difference between a data scientist and a machine learning engineer resume?
A data scientist resume usually emphasises analysis, experiments and insight. A machine learning engineer resume emphasises building, deploying and maintaining models in production, with tools like Docker, Kubernetes and pipeline orchestration.
Should I include LLM and generative AI experience?
Yes, if it is real. Say what you built, how you evaluated it and what it changed. Avoid vague claims like 'experience with AI'.
Do I need a master's degree for ML engineering roles?
Some employers ask for one, but many hire on evidence of production work. Strong projects, open-source contributions and clear results can matter as much as the degree.