ML Engineer (Senior)

Description

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 own the technical implementation and production of AI/ML solutions across the entire ML lifecycle: from model development and validation, through deployment and monitoring, to scaling and maintenance of production systems, while ensuring robust, reliable, and scalable AI solutions.

CorporateDescription

Hapag-Lloyd is one of the world’s leading liner shipping companies, connecting businesses and people across more than 600 ports worldwide . With a fleet of over 300 modern container ships and a vessel capacity of 2.5 million TEU , we keep global trade moving reliably every day.

Our global network spans 140 countries , 400 offices , and a growing portfolio of terminal and infrastructure investments. This scale enables us to deliver consistent, high‑quality service across continents and to support our customers in even the most complex supply chains.

When you join us, you become part of more than 18,000 colleagues working across borders, functions, and cultures, to not only to deliver quality for our customers, but to create innovation and opportunities across roles, regions, and perspectives.

We believe that every exploration is a chance to grow, and every port is a place to belong.

Your Journey, Our Horizon

OrganizationDescription

Knowledge Center in Gdansk

One of the goals of Hapag-Lloyd strategy, is to become more agile in the way we work and to create an environment in which we can make faster and innovative decisions. Knowledge Center in Gdansk enables the further accelerated growth, especially in the area of developing innovative digital solutions, agility and business centricity. To learn more, please check our website -

https://knowledgecenter.pl/

We offer:

• Private medical care (Medicover)
• Gym card (Multisport)
• Attractive annual bonus up to 22,5% (depending on company performance results)
• Group life insurance and employee capital plan (PPK)
• Cafeteria benefit system (cinema tickets, vouchers etc.)
• Focus on healthy lifestyle (fruit days, bike competitions, football training)
• Charity and volunteer initiatives
• Modern and well-connected office (Alchemia complex in GdaÅ„sk Oliwa)
• Relocation support (financial support, covering immigration process and polish-language lessons for non-Polish citizens)
• Internal learning management system
• Development budget (sharing the costs of certifications and conferences/ IT events)
• Flexible working hours and home office possibility (hybrid work model)

Qualifications

• 5+ years of experience in machine learning engineering with proven track record of deploying ML models to production
• Strong programming skills in Python, with expertise in ML (TensorFlow, PyTorch, scikit-learn)
• Experience in MLOps tools and practices (MLflow, Docker, CI/CD pipelines)
• Experience in data engineering tools and technologies (Databricks, Apache Spark, SQL databases), cloud platforms (AWS, Azure) and their ML services
• Hands-on experience with model serving frameworks and API development (REST, microservices architecture)
• Knowledge of monitoring and observability tools (Grafana, ELK stack)
• Ability to translate complex technical concepts for non-technical stakeholders
• Background in shipping, logistics, or similar operational industries preferred
• Ability to work in fast-moving, global environments
• Fluency in English; additional languages advantageous

Responsibilities

• Collaborate with AI Project Leads and business stakeholders to translate requirements into technical solutions
• Design, develop, and optimize machine learning models for shipping and logistics use cases
• Build robust ML pipelines for data preprocessing, feature engineering, model training, and evaluation
• Implement MLOps best practices including version control, automated testing, and continuous integration for ML workflows
• Lead production of AI/ML models, ensuring they meet enterprise-grade performance, reliability, and security standards
• Design and implement model deployment strategies including containerization, API development, and cloud-native solutions
• Build monitoring and alerting systems for model performance, data drift, and system health in production
• Establish automated retraining pipelines and model lifecycle management processes
• Mentor other team members on ML engineering best practices