Developer (f/m/d) AI in Supply Chain Management at SAP
8GBM, , Germany -
Full Time


Start Date

Immediate

Expiry Date

13 Nov, 25

Salary

0.0

Posted On

14 Aug, 25

Experience

0 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

No

Skills

Implementation Experience, English, Computer Science, Software Development, Machine Learning, Python, Data Science, Artificial Intelligence

Industry

Information Technology/IT

Description

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.

Responsibilities

Job Summary:
We are seeking a Developer (m/f/d) to join our team, focused on advancing AI in Supply Chain Management. In this role, you will be responsible for the design and implementation of features based on Machine Learning, Generative AI, and Agentic AI within our Supply Chain Management products. Collaborating closely with other development teams and stakeholders, you will help ensure our products are equipped with empowering, robust, and efficient AI capabilities.
The Role:
As a Developer (m/f/d) in our SCM Data Science team, you will work on projects that integrate AI features into our Supply Chain Management solutions. Your responsibilities include delivering specific AI features, contributing to broader initiatives that enhance our development frameworks, and ensuring seamless integration and operation of our AI-driven products.

Your tasks within developing and maintaining AI features within our Supply Chain Management solutions will include:

  • Designing implementation approach for productive AI features to bridge the gap between proof-of-concept and productive features.
  • Coding, testing, and integration of AI models and features.
  • Handling maintenance activities including error analysis and improvement on feedback.
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