Emplois en direct

We are looking for a highly skilled professional with a strong background in Machine Learning Engineering. The role is 100% remote preferably across the Middle East but open to other locations.

Responsibilities:

• Design and implement scalable ML pipelines for efficient model training, deployment, and monitoring.
• Optimize distributed training for large datasets and complex models.
• Automate workflows with CI/CD pipelines, orchestration tools (e.g., Airflow, Kubeflow), and MLOps best practices.
• Develop robust systems for real-time inferencing and edge AI deployment.
• Monitor, troubleshoot, and enhance production models for performance and reliability.
• Build and fine-tune ML models for business applications like customer segmentation, personalization, and forecasting.
• Perform advanced feature engineering and data wrangling to create high-quality datasets for modeling.
• Collaborate with stakeholders to understand business requirements and translate them into data-driven solutions.
• Analyze large datasets to extract actionable insights and recommendations.
• Contribute to A/B testing and experimental designs to evaluate model performance.
• Work closely with Data Science, Engineering, and Product teams to align project goals and ensure smooth deployment of solutions.
• Partner with MLE/MLOps colleagues to integrate models into production systems and optimize end-to-end pipelines.

Desired Qualifications:

• Bachelor’s or Master’s degree in Computer Science, Data Science, Machine Learning, or a related field.
• 4+ years of experience in MLE/MLOps roles and 2+ years in data science positions.
• Expertise in Python and ML libraries (e.g., Scikit-learn, TensorFlow, PyTorch).
• Strong experience with MLOps tools (e.g., Kubernetes, Docker, MLflow).
• Advanced SQL skills for data extraction and manipulation.
• Practical experience with cloud platforms (AWS, GCP, Azure) and big data technologies (e.g., Spark).
• Expertise in CI/CD pipelines, version control, and model monitoring.
• Proficiency in supervised and unsupervised learning algorithms (e.g., decision trees, clustering, ensemble methods).
• Experience in advanced feature engineering and data preprocessing.
• Familiarity with deep learning frameworks like TensorFlow or PyTorch.

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