Data science with AI Professional

Responsibilities:

• Design, develop, and deploy AI/ML solutions across multiple domains.
• Build and train machine learning and deep learning models using frameworks like TensorFlow, PyTorch, or Keras.
• Perform data preprocessing, feature engineering, and handle large-scale datasets for modeling and analysis.
• Apply statistical modeling, regression, classification, clustering, and ensemble methods to solve complex problems.
• Implement MLOps practices for model lifecycle management using tools such as MLflow, Kubeflow, SageMaker MLOps, or Azure ML Pipelines.
• Utilize SQL for data extraction, manipulation, and feature creation from relational databases.
• Collaborate with cross-functional teams, ensuring best practices in software development, version control (Git), code reviews, and CI/CD.
• Continuously optimize and improve models, algorithms, and pipelines for accuracy, performance, and scalability.
• Stay updated with the latest AI/ML trends and incorporate relevant advancements into solutions.