3 to 6 years Hands-on experience in developing and deploying machine learning models. Experience with DevOps practices, CI/CD pipelines, and containerization tools like Docker and Kubernetes. Conduct data preprocessing tasks, including cleaning, normalization, and feature engineering. Proficiency in programming languages such as Python, R, or Java. Experience with popular machine learning frameworks like TensorFlow, PyTorch, or scikit-learn. Evaluate model performance using metrics like accuracy, precision, recall, F1-score, and ROC-AUC Work within development environments such as Jupyter Notebooks and IDEs like PyCharm or VS Code. Well verse with API (Django / Flask) setup and integration with various workflows like Node JS application, Meta AI WhastApp or GupShup. Strong understanding of statistical concepts and machine learning algorithms. Minimum B.E/B.Tech or master’s degree in computer science or any other field, Machine Learning, Data Science, or a related field.