[Your Full Name]
[Your Phone Number] | [Your Email] | [City, State]
LinkedIn: [Your LinkedIn URL] | GitHub: [Your GitHub URL]

PROFESSIONAL SUMMARY
Machine Learning Engineer with [X] years building scalable ML systems and deploying models to production. Expert in MLOps, model optimization, and end-to-end pipeline development. Proven track record of reducing inference latency by [X%] and increasing model throughput by [Y]x.

TECHNICAL SKILLS
Programming: Python, Java, C++, Scala, Go
ML Frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost, LightGBM
MLOps & Deployment: Docker, Kubernetes, MLflow, Kubeflow, TFServing, TorchServe
Cloud Platforms: AWS (SageMaker, Lambda, ECS), GCP (Vertex AI, Cloud Run), Azure ML
Big Data: Apache Spark, Kafka, Flink, Hadoop, Hive
Databases: PostgreSQL, MongoDB, Redis, Cassandra, Elasticsearch
Model Optimization: ONNX, TensorRT, Quantization, Pruning, Distillation

PROFESSIONAL EXPERIENCE

Machine Learning Engineer / Senior ML Engineer
[Company Name], [City, State]
[Start Date] – Present
• Built and deployed [number] production ML models serving [X]M requests per day with [Y] ms p99 latency
• Designed ML pipeline architecture processing [amount] of data using Spark and Kafka
• Reduced model inference time by [X%] through optimization and quantization techniques
• Implemented CI/CD pipeline for ML models reducing deployment time from [X] days to [Y] hours
• Established monitoring and alerting system tracking model performance and data drift
• Collaborated with data scientists to productionize research models achieving [business impact]

Machine Learning Engineer
[Previous Company Name], [City, State]
[Start Date] – [End Date]
• Developed real-time recommendation system serving [X]M users with [Y%] click-through rate
• Optimized deep learning models reducing GPU costs by [X%] while maintaining accuracy
• Built feature store enabling consistent feature computation across training and serving
• Implemented A/B testing framework for evaluating [X] model variants in production
• Created data validation pipeline catching [X%] of data quality issues before model training

TECHNICAL PROJECTS

[Project Name]: Production ML Platform
• Architected end-to-end ML platform supporting [X] teams and [Y] models
• Technologies: [Kubernetes, MLflow, Airflow, PostgreSQL, etc.]
• Impact: Reduced model deployment time by [X%] and increased team productivity by [Y%]

[Project Name]: Model Serving Infrastructure
• Built scalable serving infrastructure handling [X]M requests per day
• Technologies: [TensorFlow Serving, Docker, AWS ECS, Redis, etc.]
• Impact: Achieved [X] ms p99 latency with [Y%] cost reduction

EDUCATION
[Degree Name], [Major - preferably Computer Science or related]
[University Name], [City, State]
Graduation: [Month Year]

CERTIFICATIONS
• [AWS Certified Machine Learning - Specialty or similar], [Year]
• [Kubernetes Certification], [Year]

OPEN SOURCE CONTRIBUTIONS
• [Project Name]: [Your contribution description]
• [Project Name]: [Your contribution description]