ML Engineer

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