AI/ML Engineer

You should have experience with Python-based AI/ML frameworks, Java, and/or R. Additionally, proficiency in ML frameworks such as Keras, PyTorch, Tensorflow, and libraries like scikit-learn is required. You must be familiar with working with large datasets, handling data preprocessing tasks, and exploring data to identify patterns and insights. Knowledge and practical application of statistical analysis and mathematical modeling concepts and principles are essential. Proficiency in cloud services like SageMaker, S3, EC2, Azure Machine Learning is crucial. You should be able to containerize machine learning applications for portability and reproducibility. An understanding of container orchestration for managing and scaling containers in a cloud environment is necessary. Knowledge of serverless computing for executing code without managing the underlying infrastructure is important. Experience with object storage for managing large datasets and knowledge of databases for storing and retrieving data are required. You should have the ability to provision and manage virtual machines for machine learning tasks. Familiarity with monitoring and logging tools for tracking the performance and health of models is essential. Understanding of identity and access management for securing cloud resources is important. Integration of machine learning workflows into CI/CD pipelines for automated deployment is expected. You should have the ability to optimize costs associated with cloud resources. Hands-on experience with cloud-based ML services for model training, deployment, and management is necessary. Understanding of cloud networking for secure and efficient communication between services is crucial. Proficiency in version control for collaboration and code management is required. Excellent communication skills and the ability to work effectively in a cross-functional team are essential for this role.,