Machine Learning Engineer
Model Development, Evaluation & Deployment
Supervised & Unsupervised Learning, Feature Engineering
End-to-End ML Pipelines using Scikit-learn, TensorFlow, PyTorch
API Integration and Deployment (Flask, FastAPI)
Machine Learning Engineer
As a Machine Learning Engineer, I specialize in building intelligent systems that drive data-backed decisions and automation. My expertise spans the full ML lifecycle—from problem definition to model deployment in production environments.
✅ Model Development, Evaluation & Deployment
Skilled in designing and training models for classification, regression, clustering, and time series forecasting tasks.
Proficient in cross-validation techniques, hyperparameter tuning (GridSearch, Optuna), and model performance metrics (precision, recall, F1, ROC-AUC).
Experience with model deployment on cloud platforms (AWS, Azure), as well as local and containerized environments using Docker.
🧠 Supervised & Unsupervised Learning, Feature Engineering
Deep understanding of core ML algorithms: linear/logistic regression, decision trees, random forests, gradient boosting (XGBoost, LightGBM), SVMs, and k-NN.
Experience with unsupervised learning techniques like k-means clustering, DBSCAN, and PCA.
Strong feature engineering and data preprocessing skills using pandas, NumPy, and scikit-learn to improve model accuracy and generalization.
🔁 End-to-End ML Pipelines
Build and automate ML workflows from data ingestion to model deployment using Scikit-learn Pipelines, TensorFlow Extended (TFX), and PyTorch Lightning.
Familiar with ML Ops practices: reproducibility, version control (MLflow, DVC), and monitoring of deployed models.
🌐 API Integration & Deployment (Flask, FastAPI)
Develop and expose ML models as REST APIs using Flask and FastAPI for integration into business applications.
Knowledge of CI/CD tools (GitHub Actions, Jenkins) for automating deployments and updates.
Secure, test, and document APIs for scalability and robustness.