Shape the future of SAP applications by building AI-native, HANA-optimized solutions. This role blends advanced ABAP on HANA engineering with cutting-edge agentic AI patterns. You will deliver robust, high-performance, and cloud-ready solutions, leveraging HANA pushdown principles and modern ABAP practices. The focus is on creating systems that can reason, retrieve context, and act within enterprise SAP workflows, all without the need for training foundational models from scratch.

Design and develop high-performance ABAP solutions for SAP HANA, emphasizing pushdown principles to maximize in-database processing. Construct efficient data models using CDS views and implement critical logic with AMDP/SQLScript where necessary. Develop modular, testable, and maintainable ABAP code aligned with clean architecture and enterprise standards. Build services and APIs using modern paradigms like OData, ensuring stability and contract-first behavior. Model enterprise business entities with CDS semantics for transactional and analytical use cases. Develop AI-enabled SAP experiences by integrating ABAP services with LLM capabilities, including tool calling and retrieval-augmented generation (RAG). Implement prompts, tool schemas, and context strategies to minimize hallucinations and enforce enterprise safety. Build automated tests for ABAP services and data artifacts to ensure regression coverage. Establish evaluation harnesses for AI behaviors and introduce guardrails for AI outputs. Instrument services for observability and design robust error handling for enterprise flows. Collaborate with cross-functional teams to ensure end-to-end solution correctness and performance.

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