Join our team as a Custom Software Engineer and play a pivotal role in developing innovative SAP solutions. You'll focus on building AI-native, HANA-optimized applications by integrating advanced ABAP engineering with agentic AI patterns. This role emphasizes delivering high-performance, production-grade solutions using HANA pushdown principles, modern ABAP, and governed AI capabilities.

Our approach allows us to create intelligent SAP workflows without the need for extensive foundation model training. We leverage LLMs, tools, retrieval mechanisms, and evaluation frameworks to build solutions that can reason, access context, and assist within enterprise SAP workflows. The goal is to deliver scalable, cloud-ready applications that meet specific business needs with precision and efficiency.

Design and develop ABAP solutions optimized for SAP HANA, focusing on pushdown principles to maximize in-database processing. Build efficient data models using CDS views and implement performance-critical logic with AMDP/SQLScript. Develop modular, testable ABAP code aligned with clean architecture and enterprise SDLC practices, creating stable and versioned services and APIs.

Model enterprise business entities with CDS semantics and design reusable data artifacts. Build AI-enabled SAP experiences by integrating ABAP services with LLM capabilities, including tool calling and retrieval-grounded outputs. Implement automated tests and establish evaluation harnesses for AI behaviors to ensure quality and reliability. Instrument services with logs, metrics, and traces for production support and integrate ABAP on HANA services with diverse SAP and non-SAP ecosystems.

A strong foundation in ABAP Development on HANA, including performance tuning, pushdown techniques, and efficient SQL access patterns, is essential. Proficiency with CDS views and modern data modeling practices is required, along with experience using AMDP/SQLScript for performance-critical scenarios. Candidates should demonstrate solid software engineering discipline, including clean design, a testing mindset, CI/CD orientation, and readiness for production environments. Experience with AI-native capabilities such as LLM integration, retrieval grounding, tool orchestration, evaluation loops, and establishing safety boundaries is highly desirable.