Embark on a role as a Custom Software Engineer, shaping AI-native, HANA-optimized SAP applications. You'll combine deep ABAP on HANA expertise with agentic AI patterns, including LLMs, tools, retrieval, and evaluation. This position is dedicated to delivering high-performance, cloud-ready, production-grade solutions by leveraging HANA pushdown principles, modern ABAP, and governed AI capabilities. The focus is on creating systems that can reason, retrieve context, and assist or act within enterprise SAP workflows, all without the need for training foundation models from scratch.

This role offers the opportunity to build sophisticated SAP experiences. You will integrate ABAP services with LLM capabilities, focusing on tool calling orchestration, retrieval-grounded outputs, and deterministic fallbacks. The work involves implementing prompts, tool schemas, and context assembly strategies to minimize hallucinations and enforce enterprise safety boundaries, ensuring robust and secure AI integrations.

Design and develop ABAP solutions specifically optimized for SAP HANA, employing pushdown principles to minimize data movement and maximize in-database processing. Construct efficient data models and access patterns using CDS views, associations, annotations, and authorization concepts. Implement performance-critical logic with AMDP/SQLScript, ensuring careful governance and rigorous testing.

Develop modular, testable, and maintainable ABAP code following clean architecture practices and enterprise SDLC guidelines. Build stable services and APIs using modern paradigms like OData, ensuring versioning and contract-first behavior. Consistently apply secure coding standards and authorization checks across all data and service layers.

Model enterprise business entities with CDS semantics for both transactional and analytical consumption. Design reusable data artifacts supporting downstream consumers such as UI, integrations, analytics, and AI retrieval layers. Ensure lineage-friendly structures and consistent business definitions to reduce ambiguity.

Build AI-enabled SAP experiences, integrating ABAP services with LLM functionalities. This includes tool calling orchestration, retrieval-grounded outputs, and deterministic fallbacks. Implement prompts, tool schemas, and context assembly strategies that reduce hallucinations and enforce enterprise safety boundaries.

Develop automated tests for ABAP services and data artifacts to ensure regression coverage for performance and correctness. Establish evaluation harnesses for AI behaviors, utilizing golden datasets, scenario-based tests, and online monitoring. Introduce guardrails for AI outputs, including authorization-aware context filtering, redaction rules, action confirmations, and audit logs.

Instrument services and critical logic with logs, metrics, and traces for production support and root-cause analysis. Design robust error handling for enterprise flows, incorporating retries, idempotency, correlation IDs, and safe failure modes. Support incident response with runbooks and diagnostics, continuously hardening solutions based on observed failure patterns.

Integrate ABAP on HANA services with SAP and non-SAP ecosystems using event-driven patterns, API gateways, and enterprise integration layers. Collaborate with UX, integration, data, and platform teams for end-to-end solution correctness and performance. Rapidly prototype, demonstrate solutions frequently, and evolve them into scalable production systems with security and compliance integrated.

A minimum of 3 years of relevant experience is required. Strong hands-on expertise 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 such as associations, annotations, and authorization concepts is expected.

Experience with AMDP/SQLScript for performance-critical scenarios and controlled code pushdown is necessary. Solid software engineering discipline is a must, encompassing clean design principles, a testing mindset, CI/CD orientation, and a focus on production readiness. Capability in AI native development, including LLM integration, retrieval grounding, tool orchestration, evaluation loops, and establishing safety boundaries, is a key requirement.

Beneficial skills include experience with OData/service design patterns, API lifecycle management, and contract-first development. Familiarity with integrating SAP backends with cloud runtimes and enterprise integration platforms is advantageous. Knowledge of performance benchmarking, load testing, query-plan driven tuning practices, and SAP security/authorizations for sensitive enterprise data will be highly valued.