As a Custom Software Engineer, you will be instrumental in developing innovative custom software solutions. Your role will involve designing, coding, and enhancing components across various systems and applications. You'll leverage modern frameworks and agile methodologies to deliver scalable, high-performing solutions precisely tailored to specific business requirements, embodying the role of an AI Native Tech Talent.

Design and implement governed data products on SAP BTP Datasphere, utilizing concepts like Spaces and shareable models. Build semantic models for both analytics and AI consumption, ensuring clear definitions, measures, and lineage. Create grounded AI experiences by connecting LLM applications to Datasphere models and enterprise sources. Engineer robust retrieval strategies respecting domain boundaries and access controls. Facilitate the transition from legacy warehouse investments by leveraging BW bridge patterns for phased cloud modernization. Implement layered design patterns for data landing, cleansing, and publishing, embedding quality controls throughout the delivery lifecycle. Develop data agents capable of planning, tool execution, context retrieval, and generating cited answers, with deterministic checks and fallback mechanisms. Establish comprehensive evaluation loops and add telemetry for AI interactions to enhance reliability and cost efficiency. Collaborate closely with business, data governance, and platform teams to align data products with operational workflows and drive reusable patterns.

A minimum of 2-5 years of experience is required, with a minimum of 3 years specifically in SAP BTP Datasphere for data modeling, spaces, sharing patterns, and enterprise semantic design. A strong foundation in data warehousing is essential to translate business domains into governed analytical models. Practical experience building with LLMs and RAG, encompassing retrieval, grounding, prompt/tool design, and evaluation, is a must. Solid software engineering fundamentals, including testability, a CI/CD mindset, and reliable integrations, are expected. Experience with migration/modernization leveraging BW bridge patterns, layered architecture implementation (Bronze/Silver/Gold), and familiarity with vector search/embedding pipelines are strongly beneficial.