Join our team as a Custom Software Engineer, where you'll be instrumental in developing advanced AI-native, data-centric products. This role focuses on leveraging SAP BTP Datasphere and agentic AI architectures to create intelligent data experiences, moving beyond traditional dashboards to offer conversational analytics and grounded insights. You will work with governed Datasphere models and integrated enterprise sources to build innovative solutions.

SAP Datasphere serves as a key data warehousing solution with robust integration capabilities. We are looking for skilled professionals to help us transform data into actionable intelligence and drive client value through cutting-edge technology.

Design and implement governed data products within SAP Datasphere, utilizing concepts like Spaces and shareable models for effective data exploration and transformation. Build semantic models optimized for both analytics and AI consumption, ensuring clear entity definitions and lineage. Create grounded AI experiences by integrating LLM applications with Datasphere's curated models and enterprise sources, guaranteeing traceable responses.

Engineer retrieval strategies that adhere to domain boundaries, freshness requirements, and access controls to maintain reliable AI outputs. Facilitate the modernization and migration of legacy data warehouse investments using approaches like the BW bridge pattern. Implement layered architecture designs for data landing, cleansing, and model publishing, embedding quality controls throughout the delivery lifecycle. Develop data agents capable of planning, tool execution, and generating answers with citations, supported by deterministic checks and fallback behaviors.

A minimum of 3 years of experience in software engineering is required, with a strong emphasis on SAP BTP Datasphere for data modeling, spaces, sharing patterns, and enterprise semantic design. Candidates should possess solid data warehousing fundamentals and the ability to translate business domains into governed analytical models. Hands-on experience building with LLMs and RAG, including retrieval, grounding, prompt and tool design, and evaluation is essential.

Proficiency in core software engineering principles such as testability, a CI/CD mindset, and reliable integrations is expected. Familiarity with migration and modernization efforts using BW bridge patterns, layered architecture implementation (Bronze/Silver/Gold), and vector search/embedding pipelines is highly beneficial. This role requires 15 years of full-time education.