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.

Must-have:

• Proficiency in Python (knowledge of R, C++, or Java is a plus).

• Basic skills in data processing with Pandas and NumPy.• Understanding of common ML algorithms for tabular data (e.g., Logistic

Regression, Decision Trees, Random Forest, XGBoost).

• Experience using scikit-learn for model training and evaluation.

• Familiarity with evaluation metrics such as Accuracy, F1-score, Precision, and

Recall.

• Eagerness to learn and quickly apply new AI/ML concepts.

• Good at English communication.

Nice to have:

• Basic experience with Flask/FastAPI, Docker, or cloud platforms

(AWS/GCP/Azure).

• Previous personal or academic AI/ML projects

Hands-on experience in AI software design and deployment.

• Mentorship from senior AI engineers and industry experts.

• Opportunity to work with state-of-the-art AI & ML technologies.

• Potential full-time employment after internship.

• Networking with top AI professionals and working in an international

environment.