SAP aims to infuse every SAP application with sophisticated AI capabilities, revolutionizing business operations by combining Large Language Models (LLMs) with Knowledge Graphs for business AI problems.
Requirements
- Experience in designing, developing, deploying, and testing multi-tenant business applications in the cloud with a focus on backend infrastructure and AI models
- Experience in developing data-driven microservices, APIs, CI/CD pipelines, Data pipelines, TDD, hands-on experience with docker containers and Kubernetes, familiar with one of the cloud providers.
- 3+ years working with SAP BTP platform & SAP AI Core.
- Strong knowledge in Java, Python and NodeJs.
- At least 2+ years of professional experience optimizing and serving Machine Learning models.
- Experience with Python in Linux-based environments, Git, and ML frameworks (PyTorch/TensorFlow)
- Experience in data analysis and visualization and strong knowledge in SQL or Postgres.
Responsibilities
- Work closely with our lead architect to craft solutions to the many challenges facing our Data Scientist partners.
- Work closely with SAP data scientists to understand their projects and their needs.
- Create high level requirements and designs.
- Follow and help define architectural / coding best practices and participate in software design and code reviews with the development team consisting of frontend-, backend-, full-stack, Dev-Ops and AI engineers.
- Support the product team with technical feasibility analysis, solution proposals, and ballpark effort estimations.
- Contribute to all processes of the ML lifecycle: data collection, annotation, modeling, evaluation, deployment, and monitoring
- design and implement a variety of projects around the GenAI space
Other
- Masters of science degree in Software Engineering.
- At least 7+ years of professional experience as developer.
- Ability to work in an agile environment using SCRUM methodology.
- Strong communication and collaboration skills, with the ability to work effectively in cross-cultural teams.
- Expected Travel: 0 - 10%
