Machine Learning Engineer
Caliberly
- Dubai
- Permanent
- Full-time
Problem Identification: Collaborate with stakeholders to understand business requirements and identify opportunities for applying machine learning solutions.Data Collection and Preparation: Gather and preprocess large datasets from various sources, ensuring data quality, integrity, and relevance for model training.Feature Engineering: Extract and engineer relevant features from raw data to improve model performance and accuracy.Model Development: Design, develop, and train machine learning models using techniques such as supervised learning, unsupervised learning, and deep learning.Model Evaluation: Evaluate model performance using appropriate metrics and techniques, iterating on model design and hyperparameters to optimize performance.Model Deployment: Deploy machine learning models into production environments, ensuring scalability, reliability, and maintainability.Monitoring and Maintenance: Monitor model performance in production, identify and address issues, and continuously improve model performance over time.Documentation: Document model development processes, data pipelines, and model deployment procedures for reproducibility and knowledge sharing.Collaboration: Collaborate with cross-functional teams, including data scientists, software engineers, and business stakeholders, to integrate machine learning solutions into existing systems and workflows.
Experience:
Previous experience working as a machine learning engineer or data scientist, with a proven track record of developing and deploying machine learning solutions in real-world applications.Experience with cloud platforms (such as AWS, Azure, or Google Cloud Platform) and containerization technologies (such as Docker, Kubernetes) is desirable.Experience with big data technologies (such as Apache Hadoop, Spark) and distributed computing frameworks is a plus.Experience with natural language processing (NLP), computer vision, or other specialized domains of machine learning is beneficial depending on the specific requirements of the role.