Key Responsibilities
Clean, preprocess, and analyze datasets
Implement machine learning algorithms (both supervised and unsupervised) using tools like TensorFlow, Keras, or PyTorch
Tune ML models and evaluate performance using metrics such as F1-score, ROC-AUC, etc.
Build and deploy ML pipelines using Flask, FastAPI, or Streamlit
Document experiments and collaborate on code using Git
Present findings during regular mentor reviews and receive feedback
Preferred Skills
Programming knowledge in Python and SQL
Strong understanding of machine learning concepts and model evaluation techniques
Experience with deep learning frameworks and model deployment tools
Familiarity with Git/version control systems
Benefits
Real-world project experience with measurable business outcomes
Mentorship from experienced industry professionals
Flexible working hours
Internship certificate and recommendation letter
Pre-Placement Offer (PPO) opportunity based on performance
Work Schedule
Day shift
Monday to Friday
Rotational shift
Weekend only (as per need)
Supplemental Pay
Performance bonus available
