ML Engineer
Summary
Design, build, and deploy ML models using Python, Scikit-learn, and FastAPI; engineer features, train models, and serve them via REST APIs on cloud platforms.
- Design, develop, and deploy Machine Learning models for real-world business problems.
- Collect, clean, and pre process large datasets using Python, Pandas, and NumPy.
- Perform feature engineering to improve model accuracy and performance.
- Build and optimize classification, regression, and clustering models using Scikit-learn and other ML libraries.
- Conduct data analysis and apply statistical techniques to identify trends and insights.
- Train, validate, and evaluate machine learning models using appropriate performance metrics.
- Write efficient SQL queries to extract, transform, and analyze data from databases.
- Develop and integrate REST APIs using FastAPI for model serving and application integration.
- Deploy machine learning models to cloud platforms and monitor their performance.
- Implement experiment tracking and maintain model versioning for reproducibility.
- Collaborate with data analysts, software developers, and business teams to understand project requirements.
Requirements
Python, SQL, Machine Learning, Scikit-learn, Pandas, NumPy, Feature Engineering, Model Training, Model Evaluation, Classification, Regression, Clustering, Statistics, Git, Docker, REST APIs, FastAPI, Cloud, Model Deployment and Experiment Tracking