With a global team of 5600+ IT professionals, DHL IT Services connects people and keeps the global economy running by continuously innovating and creating sustainable digital solutions. We work beyond global borders and push boundaries across all dimensions of logistics. You can leave your mark shaping the technology backbone of the biggest logistics company of the world. All our locations have earned the #GreatPlaceToWork certification, reflecting our commitment to exceptional employee experiences.
Digitalization. Simply delivered.
At DHL IT Services, we are designing, building and running IT solutions for the whole DPDHL globally.
The AI & Analytics team builds and runs solutions to get much more value out of our data. We help our business colleagues all over the world with machine learning algorithms, predictive models and visualizations. We manage more than 46 AI & Big Data Applications, 3.000 active users, 87 countries and up to 100,000,000 daily transaction.
Integration of AI & Big Data into business processes to compete in a data driven world needs state of the art technology. Our infrastructure, hosted on-prem and in the cloud (Azure and GCP), includes MapR, Airflow, Spark, Kafka, jupyter, Kubeflow, Jenkins, GitHub, Tableau, Power BI, Synapse (Analytics), Databricks and further interesting tools.
We like to do everything in an Agile/DevOps way. No more throwing the “problem code” to support, no silos. Our teams are completely product oriented, having end to end responsibility for the success of our product.
Ready to embark on the journey? Here’s what we are looking for:
Currently, we are looking for MLOps/AI Engineer. In this role, you will have the opportunity to design and develop solutions, contribute to roadmaps of Big Data architectures and provide mentorship and feedback to more junior team members. We are looking for someone to help us manage the petabytes of data we have and turn them into value.
Does that sound a bit like you? Let’s talk! Even if you don’t tick all the boxes below, we’d love to hear from you; our new department is rapidly growing and we’re looking for many people with the can-do mindset to join us on our digitalization journey. Thank you for considering DHL as the next step in your career – we do believe we can make a difference together!
What will you need?
- University degree in a quantitative field (e.g. Mathematics, Statistics, Computer Science, Engineering, or related discipline) or equivalent experience.
- Hands on experience in data science, machine learning, or AI engineering, ideally contributing to or leading end to end projects from concept to deployment.
- Strong programming skills in Python (preferred) and solid experience with SQL. Familiar with software development best practices (version control, testing, code reviews, CI/CD)
- Proven experience in building backend services, data pipelines, or integrating ML/LLM components into production environments.
- Working knowledge of retrieval and RAG systems, agent orchestration, and solid understanding of the AI/LLM ecosystem and related frameworks.
- Strong understanding of machine learning fundamentals and experience developing and deploying models that deliver real business value.
- Hands on experience with modern ML/AI frameworks (e.g. PyTorch, scikit learn) and good grasp of MLOps workflows for scalable model deployment.
- Experience with cloud platforms (e.g. AWS, Azure, or GCP) and distributed data environments.
- Excellent communication skills in English.
- Ability to clearly explain technical trade offs and decisions to both technical and non technical audiences.
- Awareness of Agile / Scrum ways of working
- Excellent problem-solving skills and ability to work in a collaborative environment
You should have:
- Github
- Kubernetes
- Kubeflow
- Docker
- Python (Preferably ML knowledge)
- Github Actions / Jenkins
- Kibana / Grafrana
- Linux/Bash
Language requirements:
- English – Fluent spoken and written (C1 level)
An array of benefits for you:
- Hybrid work arrangements to balance in-office collaboration and home flexibility.
- Annual Leave: 42 days off apart from Public / National Holidays.
- Medical Insurance: Self + Spouse + 2 children. An option to opt for Voluntary Parental Insurance (Parents / Parent -in-laws) at a nominal premium covering pre existing disease.
- In House training programs: professional and technical training certifications.
