SAP Senior Applied AI Data Engineer f/m/d 

Germany, Baden-Württemberg 

574657437

What you`ll do

The Role:

• Work with a global, agile team of data scientists, AI/ML engineers and software engineers on AI and big data pipelines as well as on the E2E lifecycle
• Support the development process of cutting edge data science and AI solutions and lead the development, maintenance, optimization and migration of data infrastructure and ETL processes across business domains
• Introduce AI, data and software engineering best practices along the AI and big data application lifecycle in close collaboration with our Data Scientists and ML Developers
• Execute enterprise-grade Data- and MLOps: continuous integration, continuous delivery, and continuous training for our ML models and data pipelines
• Drive architectural decisions for enterprise grade AI solutions
• Keep up-to-date with state-of-the-art algorithms and technologies in AI and data engineering and act as a thought leader in the AI and data engineering space

What you bring

We are looking for a driven and motivated team player with at least a Master's Degree in Computer Science, Data Engineering, or related fields. You should have multiple years of experience in Python programming and in distributed computing frameworks such as Apache Spark. Additionally, solid experience with data modeling, ETL processes, and cloud infrastructure is required. A strong background with a wide range of ML/AI algorithms, statistical models and ML architectures rounds off the profile.

In more detail, candidates should have exceptional skills in:

• Big Data processing and analytics (e.g., Apache Spark, Databricks)
• Broad knowledge of MLOps best practices and the AI Lifecycle
• Cloud platforms and services (e.g., GCP, Azure, BTP)
• Data warehousing (e.g. Bigquery, SAP BW)
• Data pipeline development and optimization
• Enterprise grade AI best practices and architectures
• Regular usage of software version control and automation tools (e.g., Git, Jenkins, etc.)
• Comprehensive understanding of GitOps/DataOps best practices