WE HELP THE WORLD RUN BETTER

At SAP, we enable you to bring out your best. Our company culture is focused on collaboration and a shared passion to help the world run better. How? We focus every day on building the foundation for tomorrow and creating a workplace that embraces differences, values flexibility, and is aligned to our purpose-driven and future-focused work. We offer a highly collaborative, caring team environment with a strong focus on learning and development, recognition for your individual contributions, and a variety of benefit options for you to choose from.

EDUCATIONAL AND EXPERIENCE REQUIREMENTS

Required

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related technical field.
• 0–3 years of experience in software development, preferably in AI or cloud-based applications.
• Solid understanding of computer science fundamentals, including algorithms, data structures, object-oriented design, and software architecture.
• Understanding of machine learning fundamentals, pipelines, and data lifecycle management.
• Practical experience with ML or data processing frameworks (e.g., PyTorch, Spark).
• Proficiency in at least two programming languages, preferably Python and Java.
• Familiarity with containerization and orchestration technologies (e.g., Docker, Kubernetes).
• Strong problem-solving mindset and motivation to deliver customer value.
• Excellent written and verbal communication skills in English.

Optional

• Hands-on scripting experience (e.g., bash).
• Skills in modern web development (JavaScript, HTML5, CSS)
• Operational familiarity with Linux systems (Red Hat, Ubuntu, etc.).
• Experience with cloud platforms such as SAP BTP, AWS, Azure, or GCP.
• Experience working in Agile/Scrum teams.

Job Segment: Developer, Cloud Applications, Cloud Operations, Machine Learning, Artificial Intelligence, Data science development

• Build and maintain scalable data pipelines for machine learning workflows focused on document processing.
• Manage large-scale structured and unstructured data assets, ensuring quality and readiness for training.
• Develop tools for data annotation and synthetic data generation to support model development.
• Support model training, validation, and deployment for document classification, extraction, and layout analysis.
• Monitor and improve model performance through continuous evaluation and retraining.
• Automate ML and data workflows for efficiency and reliability in cloud environments.
• Translate business requirements into effective ML solutions with measurable outcomes.
• Collaborate with cross-functional teams and stakeholders to align data and model strategies.
• Ensure integration quality and operational readiness across global SAP environments.
• Quickly learn domain-specific document types and adapt to evolving AI technologies