Mid Cloud Engineer (m/f/d)

at  KuehneNagel

As one of the world’s leading logistics companies, we have opened a new IT Centre in Porto, Portugal. We aim to accelerate digital technologies for our global customers, focusing on developing new mission-critical products, e-solutions, and new digital services. Join our fascinating and international working environment and become part of the team responsible for our digital logistics processes in line with the Kuehne+Nagel strategy.

YOUR SKILLS AND EXPERIENCES

• Working software is not good enough - you value maintainable code and strive for solutions that minimize complexity;
• Interpersonal skills to discuss technical concepts with colleagues and effectively convey them in writing;
• Collaborative work in an international team inspires you;
• Experience in Python or similar languages;
• Experience with at least one cloud provider;
• Bonus points for experience with big data and compute-intensive application;
• Bonus points for experience with Kubernetes.

YOUR ROLE

The AI engineering team focuses on improving the productivity of our data scientists with their engineering tasks. For this, we follow two approaches:- Collaborate with other product teams to remove friction from their engineering processes.- Carry out long-term foundational projects that serve as building blocks for the whole data science department.It provides the perfect opportunity to interact with experts on machine learning, statistics, and data analysis and to be involved in delivering impactful and reliable products to our customers. You ensure that engineering practices are followed, improving the execution quality and speed of our engineering tasks and unblocking our data scientists.

YOUR RESPONSIBILITIES

• Build tools and implement processes to improve the developer experience of our data scientists working on AI products in collaboration with the respective product teams;
• Ensure that cloud-based infrastructure and services are utilized efficiently across data science products and projects;
• Bring ideas and potential technologies to evaluation for their applicability within our context;
• Champion practices like infrastructure-as-code, continuous deployment, and repoducibility within the data science team and integrate it seamlessly with surrounding infrastructure and processes.