Senior ML Engineer

Symphony Solutions is a Cloud- and AI-driven technology company headquartered in the Netherlands, delivering both world-class services and innovative products. With a remote-first mindset, the company has a global presence spanning over 20 countries. It provides custom software solutions for Airline, Healthcare, iGaming, E-learning, e-Commerce, and Supply Chain sectors.

• 4+ years of hands-on experience in machine learning engineering.
• Strong proficiency in Python and core ML frameworks (e.g., PyTorch, TensorFlow, scikit-learn, XGBoost).
• Solid experience with deep learning — architecture design, training, hyperparameter tuning, and deployment of neural network models.
• Proven experience designing and deploying recommender systems.
• Hands-on experience with AWS SageMaker and broader AWS ML ecosystem.
• Practical experience setting up data processing and ML workflows on AWS.
• Strong MLOps skills.
• Solid understanding of the full ML lifecycle.
• Hands-on experience with containerization and orchestration in production environments.
• Proficiency with SQL and experience working with both structured and unstructured data sources.
• Strong problem-solving skills with an emphasis on scalability and performance optimization.

• Design, train, and iterate on ML and deep learning models for recommendation, ranking, and personalization use cases.
• Architect and maintain end-to-end ML pipelines on AWS.
• Set up and optimize data processing and ML workflows using AWS services.
• Build and maintain MLOps infrastructure.
• Collaborate with data engineers to ensure data quality, build feature stores, and prepare datasets for model training and inference.
• Evaluate and benchmark model performance, run offline and online experiments, and drive continuous improvement of model accuracy and efficiency.
• Optimize model serving infrastructure for latency, throughput, and cost-effectiveness.
• Partner with product and business stakeholders to translate requirements into well-scoped ML solutions.
• Document model architecture, assumptions, performance characteristics, and known limitations.
• Stay current with advances in recommendation systems, deep learning, and cloud ML services, and propose improvements to existing approaches.