AI , Machine Learning & Python Development Trainer

Role & responsibilities

• Deliver structured, interactive sessions on

  Python programming

  , Machine Learning, and core AI concepts.
• Develop and update training materials including lectures, coding exercises, real-world datasets, quizzes, and capstone projects.
• Teach core

  Python programming fundamentals

  , including:
• Data types, control structures, functions, OOPs concepts
• File handling, error handling, and standard libraries
• Working with popular frameworks (e.g., Flask, FastAPI) for basic app development and model deployment
• Teach essential

  ML topics

  , including:
• Supervised & unsupervised learning (regression, classification, clustering)
• Data preprocessing, feature selection, and transformation
• Model evaluation, performance tuning, pipelines
• Ensemble methods (e.g., Random Forest, XGBoost)
• Model deployment fundamentals
• Cover

  foundational AI topics

  , such as:
• Basics of neural networks and deep learning
• Fundamentals of Natural Language Processing (NLP)
• Introduction to Computer Vision (CV)
• Using pre-trained models and transfer learning
• Provide hands-on coding sessions using Python tools and libraries: Pandas, NumPy, Scikit-learn, TensorFlow, Keras, PyTorch.
• Mentor trainees through practical assignments, project implementation, and real-world scenarios.
• Stay up-to-date with industry trends and integrate relevant topics into the curriculum.

Preferred candidate profile

• Strong

  Python programming skills

  with practical development experience.
• Hands-on expertise in ML libraries (Scikit-learn, XGBoost, Pandas, NumPy) and AI frameworks (TensorFlow, Keras, PyTorch).
• Familiarity with building and deploying Python applications using frameworks like Flask or FastAPI.
• Solid understanding of data pipelines, model evaluation, and basics of deployment.
• Exposure to version control (Git) and Jupyter notebooks.
• Cloud-based ML tools experience (AWS, Azure, or GCP) is an advantage.