Description :

Job Summary :

We are looking for a Machine Learning Engineer with strong experience in classification problems to build, optimize, and deploy scalable ML models.

The ideal candidate should have hands-on experience across the end-to-end ML lifecycle, from data preprocessing to production deployment.

Key Responsibilities :

- Design, develop, and optimize classification models (binary & multi-class).

- Perform data preprocessing, feature engineering, and feature selection.

- Train, evaluate, and tune ML models using appropriate metrics (Precision, Recall, F1, ROC-AUC).

- Handle imbalanced datasets and apply relevant techniques.

- Deploy models into production and monitor performance.

- Collaborate with data engineers, product teams, and stakeholders.

- Perform model debugging, error analysis, and continuous improvement.

- Write clean, maintainable, and well-documented code.

Required Skills & Qualifications :

- 4+ years of hands-on experience in Machine Learning.

- Strong understanding of classification algorithms :

1. Logistic Regression.

2. Decision Trees, Random Forest.

3. XGBoost / LightGBM / CatBoost.

4. SVM.

- Proficiency in Python.

- Strong experience with scikit-learn, NumPy, Pandas.

- Good understanding of ML evaluation metrics.

- Experience working with real-world noisy datasets.

- Knowledge of data leakage and model bias issues.

- Familiarity with SQL for data analysis.

- Strong problem-solving and analytical skills.

Good to Have :

- Experience with Deep Learning frameworks (TensorFlow / PyTorch).

- Exposure to ML deployment (Flask, FastAPI, Docker).

- Experience with cloud platforms (AWS / GCP / Azure).

- Knowledge of MLOps tools (MLflow, Airflow, Kubeflow).

- Basic understanding of statistics and probability.