Machine Learning Engineer Resume Example

Architected and deployed a real-time recommendation system using collaborative filtering and deep learning, increasing user engagement by 28% and generating $2.3M in additional revenueOptimized inference pipeline using TensorRT and ONNX Runtime, reducing model latency from 450ms to 180ms while maintaining 94% accuracyBuilt and maintained MLOps infrastructure using Kubernetes, MLflow, and Airflow to automate model training, versioning, and deployment across 15+ production modelsLed migration of legacy models to PyTorch Lightning framework, improving training speed by 40% and reducing GPU costs by $18K annuallyMentored 3 junior engineers on ML best practices, code reviews, and model evaluation techniques

Developed NLP-based sentiment analysis system processing 500K+ customer reviews daily, achieving 89% F1-score using BERT and transformer architecturesImplemented computer vision models for automated quality inspection, reducing manual review time by 65% and catching defects with 92% precisionCreated feature engineering pipeline using PySpark that processed 10TB+ of data daily, improving model training efficiency by 3xCollaborated with product and engineering teams to integrate ML models into customer-facing applications serving 2M+ active usersEstablished A/B testing framework for model evaluation, enabling data-driven decisions for 12+ product features

Built predictive maintenance models using time-series analysis and XGBoost, reducing equipment downtime by 23% and saving $500K in operational costsDeveloped data preprocessing and feature extraction pipelines using Pandas and Scikit-learn for structured and unstructured dataImplemented automated model monitoring system to track performance drift, triggering retraining workflows when accuracy dropped below 85%Contributed to open-source ML projects and maintained internal Python libraries used by 20+ data scientists