Responsibilities
· Design, develop, and implement machine learning models for various applications, including personalized learning paths, content recommendation, and student performance prediction
· Preprocess and analyze large datasets to extract meaningful features for model training
· Develop and maintain scalable ML pipelines for data processing, model training, and deployment
· Design and implement robust model deployment strategies, including containerization and orchestration of ML models
· Set up and manage continuous integration and continuous deployment (CI/CD) pipelines for ML models
· Implement monitoring systems to track model performance, data drift, and system health in production environments
· Collaborate with AI engineers, software engineers, and product managers to integrate ML solutions into our platform
· Troubleshoot and debug issues in production ML systems
Skills & Experience
· 2-4 years of experience in developing and implementing machine learning solutions
· Strong proficiency in Python and experience with ML libraries such as scikit-learn, TensorFlow, and PyTorch
· Solid understanding of machine learning algorithms, including supervised and unsupervised learning techniques
· Proven experience in deploying ML models to production environments
· Knowledge of MLOps practices and experience with ML model deployment, monitoring, and maintenance
· Experience with AWS cloud technologies, especially AWS SageMaker or other familiar cloud & ML services (e.g. Google Cloud AI Platform, or Azure Machine Learning)
· Experience with Generative AI and natural language processing (NLP) is a plus
· Bachelor's degree or higher in Computer Science, Machine Learning, Statistics, or a related field.
