Custom Software Engineer

Custom Software Engineer

Develop custom software solutions to design, code, and enhance components across systems or applications. Use modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.

Must have skills: SAP BTP Datasphere

Minimum 3 year(s) of experience is required. Educational Qualification: 15 years full time education.

Summary: Build AI native, data centric products on SAP BTP Datasphere by combining strong enterprise data warehousing and semantic modeling expertise with agentic AI architectures (LLMs + tools + retrieval + evaluation). The focus is to move beyond dashboards into intelligent data experiences—data agents, conversational analytics, and grounded insights—built on governed Datasphere models and integrated enterprise sources.

SAP Datasphere is positioned as a data warehousing solution with integration capabilities. Core Responsibilities:

• AI Native Data Product Engineering (on Datasphere)
• Retrieval + Grounding (RAG) over Enterprise Data
• Hybrid Modernization & Migration (BW bridge patterns)
• Lakehouse style Layering & Data Quality by Design
• Agentic Orchestration & Tooling
• Evaluation, Observability & Responsible AI
• Integration & Collaboration

Primary Skills (AI Native Must Have): SAP BTP Datasphere: data modeling, spaces, sharing patterns, enterprise semantic design. Strong data warehousing fundamentals and ability to translate business domains into governed analytical models. Hands-on building with LLMs + RAG (retrieval, grounding, prompt/tool design, evaluation). Solid software engineering fundamentals: testability, CI/CD mindset, reliable integrations.

Secondary / Strongly Beneficial Skills: Migration/modernization experience leveraging BW bridge style transition patterns. Layered architecture implementation (Bronze/Silver/Gold) for scalable analytics delivery. Familiarity with vector search / embedding pipelines (when integrating external AI retrieval components).

What This Role Does Not Center On: Training foundation models from scratch (the emphasis is on building agentic apps and governed retrieval on enterprise data). AI assisted only delivery this role owns the AI behavior (grounding, evaluation, safety) end to end.

Value Delivered: Faster path from data to decision through conversational + agentic analytics grounded in governed Datasphere models. Scalable modernization of hybrid data estates via patterns like BW bridge. Higher trust AI outputs by implementing layered quality + evaluation loops.

Additional Information: A 15 years full time education is required. 15 years full time education.