Requirements

• Assist in collecting, cleaning, validating, and preparing data for training and evaluation.
  
• Support the design, development, and tuning of machine learning and deep learning models.
  
• Contribute to scalable and reusable data pipelines using modern ML workflows.
  

• Conduct experiments and benchmarking exercises to test model performance.
  
• Perform error analysis, feature importance, and other model diagnostics.
  
• Track and log training/testing outcomes to support reproducibility and model versioning.
  

• Help build and integrate AI-powered APIs, scripts, and microservices.
  
• Collaborate on backend services and model deployment in dev/test environments.
  
• Use Git, CI/CD tools, and containerization (e.g., Docker) to maintain codebase quality.
  

• Work on projects that involve Natural Language Processing (NLP), Computer Vision, Generative AI, or Recommendation Systems.
  
• Support annotation, feature engineering, and augmentation tasks where necessary.
  

• Write clear, well-organized documentation for code, models, datasets, and project workflows.
  
• Participate in team meetings, sprint planning, and code reviews.
  
• Engage with mentors to reflect on progress, set learning goals, and track outcomes.
  

• Programming proficiency in Python and common ML libraries such as:
  
• Pandas, NumPy, Scikit-learn
  
• TensorFlow, PyTorch, or similar
  
• Experience with Jupyter Notebooks and version control (Git/GitHub)
  
• Basic understanding of supervised/unsupervised learning, neural networks, or clustering
  

• Ability to interpret data trends, visualize outputs, and debug model behaviour