AI Engineer Intern

• Work with tabular/structured data: Data cleaning, feature engineering, and exploratory data analysis (EDA) using tools like Pandas, NumPy, and visualization libraries (e.g., Matplotlib, Seaborn).
• Develop and optimize ML models for tasks such as classification, regression, or risk prediction (e.g., loan approval, credit scoring).
• Implement ML pipelines: Automate data processing, training, validation, and model evaluation.
• Collaborate with engineers and data scientists to integrate models into production environments (APIs, microservices).
• Support deployment and monitoring of ML models (using tools like Docker, Kubernetes, MLflow, or cloud services).
• Research and prototype new features or algorithms to improve system accuracy and efficiency.
• Document and communicate results clearly in code and presentations. 

Technical Skills 

• Good knowledge of Python (and/or R, C++/Java is a plus). 
• Hands-on experience with data processing/analysis using Pandas, NumPy; able to handle missing data, outliers, feature selection, encoding, etc. 
• Basic understanding of Machine Learning algorithms: Especially those suitable for tabular data (e.g., Logistic Regression, Decision Trees, Random Forest, XGBoost, CatBoost, LightGBM, SVM). 
• Familiarity with ML frameworks: scikit-learn, and optionally PyTorch or TensorFlow for more advanced tasks. 
• Knowledge of model evaluation metrics: Accuracy, AUC, F1, Precision/Recall, etc. 
• Familiar with version control (Git) and basic software development workflows. 
• Willingness to learn and apply new AI/ML concepts quickly. 
• Good at English communication 

Nice to Have (Plus) 

• Experience in AI model deployment (Flask/FastAPI, Docker, or cloud platforms: AWS/GCP/Azure). 
• Familiar with MLOps concepts, CI/CD for ML, or tools like MLflow, DVC. 
• Prior participation in Kaggle competitions or ML projects (especially those with tabular data). 
• Knowledge of SQL/NoSQL databases for data querying. 
• Exposure to business problems in finance, banking, or loan risk modeling is a strong plus. 

After application screening, the next step will be a telephone interview with a member of our HR team. If successful, the final stage is face-to-face interview that will take place in our office.