- 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 productive 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

📌 ML Engineer (Noida)
🏢 Training Basket
📍 Noida