Software Engineer for ML and Cloud Solutions

As part of AI Hub within Data Insights & AI Department (DIA), you will lead Hapag-Lloyd's AI transformation initiatives and AI adoption. Contributing to AI implementation across our organization, you will create new business value with AI technology for shipping and logistics applications for our 17,000+ colleagues.

In short

you will build, deploy, and operate cloud-native AI/ML services on AWS with a strong focus on Python engineering, Cloud DevOps, Linux-based runtime environments, and production operations. This role is about productionalization and platform/service engineering – not about ML research, model experimentation, or leading model design.

• Collaborate closely with Data Scientists and Data Engineers to productionalize processing and inferencing code, models and data products
• Collaborate with AI Project Leads and business stakeholders to translate requirements into production-ready cloud solutions
• Develop and maintain Python-based services, pipelines, and automation for AI/ML use cases
• Design, build, and operate AWS/Databricks infrastructure for AI/ML workloads
• Implement Cloud DevOps practices: CI/CD pipelines, infrastructure-as-code, automated deployments, and environment management
• Build and operate containerized workloads and API-based integrations
• Implement monitoring, alerting, and operational dashboards for service health, cost, and ML-related signals (e.g., data drift)
• Support model lifecycle operations (versioning, deployment, rollback, retraining automation) in collaboration with Data Scientists
• 5+ years of experience in Python engineering for production systems
• Strong AWS hands-on experience with services like VPC, EC2, S3, RDS, Lambda, CloudWatch ECS/EKS
• Proven Cloud DevOps experience: CI/CD pipelines, Git-based workflows, infrastructure-as-code (Terraform), release management
• Solid Linux skills (shell, networking basics, troubleshooting, logs/metrics, process and resource management)
• Experience with containerization and runtime operations
• Experience with Python ML libraries or eagerness to learn on those (PyTorch, scikit-learn)
• Experience with data engineering tools and technologies like Databricks, Apache Spark, SQL databases is a plusMLOps experience like MLflow, model serving, monitoring concepts is a plus
• Ability to translate complex technical concepts for non-technical stakeholders
• Ability to work in interdisciplinary team of various AI competencies
• Fluency in English