Machine learning engineer in logistics

An opportunity is available for an experienced Senior Machine Learning Engineer to develop, deploy, and optimise large-scale machine learning systems within a global technology and delivery platform. The position focuses on building production-ready ML solutions that improve logistics operations, demand forecasting, delivery time predictions, pricing, and operational efficiency.

• Design, develop, and deploy scalable machine learning models and applications.
• Research and implement advanced ML techniques to solve complex logistics and forecasting challenges.
• Develop predictive models for delivery time estimation, supply and demand forecasting, and pricing optimisation.
• Build and maintain production-grade machine learning pipelines and infrastructure.
• Develop high-performance Python applications and services.
• Deploy and manage machine learning solutions using cloud platforms such as AWS or Google Cloud Platform.
• Work with real-time data streaming technologies and event-driven architectures.
• Develop and optimise microservices and cloud-native applications.
• Implement tools, data pipelines, and evaluation frameworks to support ML experimentation and deployment.
• Monitor and improve model performance, scalability, and reliability.
• Collaborate with Data Scientists, Software Engineers, Product Managers, and other technical stakeholders.
• Contribute to engineering best practices, technical design, and continuous improvement initiatives.

• Strong professional experience in Machine Learning Engineering, including developing and deploying production ML systems.
• Advanced Python programming and software engineering skills.
• Experience developing real-time data streaming solutions for forecasting, logistics, or similar applications.
• Proven ability to design, deploy, and maintain scalable ML solutions in production environments.
• Experience with AWS, Google Cloud Platform, or comparable cloud technologies.
• Strong understanding of microservices, event-driven architecture, serverless computing, and cloud architecture patterns.
• Experience deploying applications using Kubernetes.
• Experience working with large-scale, real-time production systems handling millions of requests or transactions.
• Strong problem-solving skills and the ability to work independently on complex technical challenges.
• Experience collaborating with cross-functional engineering and product teams.
• Будет плюсом: Experience with Docker, Airflow, MLflow, or Apache Flink; Knowledge of MLOps practices and automated machine learning deployment pipelines; Experience with logistics optimisation, demand forecasting, or dynamic pricing; Experience developing high-throughput, low-latency ML applications; Background in e-commerce, online marketplaces, delivery platforms, or logistics technology

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