[Your Name]
[Your Address]
[City, State ZIP Code]
[Your Email]
[Your Phone Number]
[LinkedIn Profile URL]
[GitHub Profile URL]

PROFESSIONAL SUMMARY
Machine Learning Engineer with [X] years developing and deploying ML models in production. Expert in [deep learning, NLP, computer vision, recommendation systems] with proven track record of [key achievement]. Specialized in building scalable ML pipelines serving [millions of predictions, improving metrics by X%].

TECHNICAL SKILLS
Languages: Python, SQL, [e.g., R, Java, Scala]
ML/DL Frameworks: [e.g., TensorFlow, PyTorch, Scikit-learn, Keras, XGBoost]
MLOps: [e.g., MLflow, Kubeflow, SageMaker, Airflow]
Cloud ML: [e.g., AWS SageMaker, GCP AI Platform, Azure ML]
Data Processing: [e.g., Pandas, NumPy, Spark, Dask]
Deployment: [e.g., Docker, Kubernetes, FastAPI, Flask]
Monitoring: [e.g., Prometheus, Grafana, Weights & Biases]
Specialties: [e.g., NLP, Computer Vision, Time Series, Recommendation Systems]

PROFESSIONAL EXPERIENCE

[ML Engineer Title]
[Company Name], [City, State]
[Month Year - Present or Month Year]
- [Developed and deployed recommendation system increasing user engagement by X% and revenue by $Y]
- [Built NLP model for sentiment analysis processing X documents with Y% accuracy]
- [Implemented computer vision model for object detection achieving X mAP on validation set]
- [Created ML pipeline for feature engineering, training, and deployment serving X predictions per second]
- [Reduced model inference time from X ms to Y ms through optimization and model compression]
- [Implemented A/B testing framework measuring model impact on X business metrics]
- [Set up monitoring and alerting for model performance and data drift]

[Previous ML Role]
[Company Name], [City, State]
[Month Year - Month Year]
- [Trained deep learning models on X dataset achieving Y% improvement over baseline]
- [Implemented automated hyperparameter tuning reducing model development time by X%]
- [Built data preprocessing pipeline handling X million samples]
- [Deployed models to production using Docker/Kubernetes serving X requests daily]
- [Created model explainability tools using SHAP/LIME for stakeholder transparency]
- [Collaborated with data engineers to build feature stores and data pipelines]

KEY PROJECTS & MODELS

[Model/System Name]
Problem: [Business problem solved]
Approach: [ML technique used]
Results: [Quantified impact]
- [Detailed description of model architecture and implementation]
- [Training data size, features used, model performance metrics]
- [Production deployment details and serving infrastructure]
- [Business impact: increased revenue, reduced costs, improved efficiency]

[Another Model/System]
Problem: [Problem domain]
Approach: [Techniques applied]
Results: [Measurable outcomes]
- [Technical implementation details]
- [Model performance and validation approach]
- [Deployment and monitoring strategy]

PUBLICATIONS & RESEARCH
- [Paper title], [Conference/Journal], [Year]
- [Blog posts or technical articles on ML topics]
- [Kaggle competitions: rank, competition name]

EDUCATION
[Degree Name] in [Computer Science, Statistics, Mathematics, related field]
[University Name], [City, State]
Graduated: [Month Year]
Thesis: [If relevant and impressive]

CERTIFICATIONS
- [TensorFlow Developer Certificate], [Year]
- [AWS Machine Learning Specialty], [Year]
- [Deep Learning Specialization], [Platform], [Year]