Machine Learning Engineer

Talemexa is seeking a Machine Learning Engineer to design, develop, deploy, and maintain production-grade machine learning systems. The role focuses on predictive modeling, personalization, ranking, and recommendation systems across the machine learning lifecycle, in collaboration with data science, infrastructure, and product teams.

Responsibilities:

• Design, develop, train, and implement machine learning models for prediction, personalization, ranking, and recommendation
• Build reliable data preparation pipelines to process, clean, transform, and validate datasets
• Perform feature engineering to improve model performance and predictive accuracy
• Train, evaluate, and optimize machine learning models using appropriate algorithms, metrics, and frameworks
• Use Python, SQL, and machine learning libraries such as PyTorch, TensorFlow, or Scikit-learn to develop practical solutions
• Deploy trained models into production environments and integrate them with existing applications and services
• Develop automated workflows for model monitoring, performance evaluation, and retraining
• Track model quality and identify issues such as data drift, performance degradation, and inconsistent predictions
• Optimize model inference speed, computational efficiency, scalability, and resource utilization
• Work with cloud infrastructure and MLOps tools to streamline model deployment and lifecycle management
• Use containerization technologies such as Docker to support consistent development and deployment environments
• Collaborate with data scientists, infrastructure engineers, and product teams to align technical solutions with business requirements
• Maintain technical documentation covering model architecture, training processes, deployment procedures, and performance metrics
• Follow responsible AI practices and promote reliability, transparency, and appropriate data usage

Requirements:

• Bachelor's or master's degree in Computer Science, Machine Learning, Mathematics, Data Science, or a related field, or equivalent practical experience
• 3–6 years of professional experience in machine learning engineering or a related technical discipline
• Strong proficiency in Python and practical experience using SQL
• Hands-on experience with PyTorch, TensorFlow, Scikit-learn, or comparable machine learning frameworks
• Solid understanding of statistical modeling, machine learning algorithms, model evaluation, and optimization techniques
• Experience deploying and maintaining machine learning models in production environments
• Familiarity with cloud computing platforms, containerization, and MLOps workflows
• Understanding of data pipelines, feature engineering, model monitoring, and model retraining
• Strong analytical thinking, debugging, and problem-solving abilities
• Effective communication skills and the ability to collaborate across technical and product teams
• Python Programming
• Machine Learning and Predictive Modeling
• PyTorch and TensorFlow
• Scikit-learn
• SQL and Data Processing
• Feature Engineering and Data Pipelines
• Model Evaluation and Optimization
• MLOps and Model Lifecycle Management
• Production Model Deployment
• Docker and Containerization
• Cloud Computing and Infrastructure
• Model Monitoring and Retraining
• Performance Optimization and Scalability
• Responsible AI Practices
• Technical Documentation and Team Collaboration