Join our team as a Custom Software Engineer and play a pivotal role in developing advanced SAP applications. This position focuses on building AI-native, HANA-optimized solutions by integrating deep ABAP on HANA engineering with agentic AI patterns.

You will deliver high-performance, cloud-ready, production-grade solutions. The role emphasizes leveraging HANA pushdown principles, modern ABAP development, and governed AI capabilities to reason, retrieve context, and assist within enterprise SAP workflows, without the need for training foundation models from scratch.

Design and develop ABAP solutions optimized for SAP HANA, utilizing pushdown principles to maximize in-database processing. Build efficient data models and access patterns with CDS views, associations, and annotations. Implement modular, testable, and maintainable ABAP code adhering to clean architecture practices. Develop services and APIs using modern paradigms like OData, ensuring stability and contract-first behavior. Model enterprise business entities using CDS semantics and design reusable data artifacts. Build AI-enabled SAP experiences integrating ABAP services with LLM capabilities, including tool calling and retrieval-grounded outputs. Develop automated tests for ABAP services and data artifacts, and establish evaluation harnesses for AI behaviors. Instrument services with logs, metrics, and traces for production support and root-cause analysis. Integrate ABAP on HANA services with SAP and non-SAP ecosystems, collaborating with cross-functional teams to ensure end-to-end solution correctness and performance.

A minimum of 3 years of experience is required, with a preference for 2-5 years. A strong foundation in ABAP Development on HANA, including performance tuning and efficient SQL access, is essential. Proficiency with CDS views and modern data modeling practices is expected. Experience with AMDP/SQLScript for performance-critical scenarios is also required. Solid software engineering discipline, including clean design, a testing mindset, CI/CD orientation, and a focus on production readiness, is crucial. Demonstrated capability in AI native builds, encompassing LLM integration, retrieval grounding, tool orchestration, evaluation loops, and safety boundaries is a key requirement. Secondary skills such as OData/service design patterns, API lifecycle management, and experience with cloud runtimes or enterprise integration platforms are highly beneficial.