Custom Software Engineer

Project Role : Custom Software Engineer Project Role Description : 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 BW/4HANA Data Modeling & Development Good to have skills : NA Minimum 5 year(s) of experience is required Educational Qualification : 15 years full time education Summary AI Powered Tech Talent. Build AI native, data warehouse and analytics solutions on SAP BW/4HANA, combining deep BW/4HANA modeling expertise with agentic AI architectures (LLMs + tools + retrieval + evaluation). The focus is to evolve enterprise BI from reporting and dashboards to grounded, conversational analytics and data agents that can reason over governed models, retrieve context, and deliver explainable outcomes. This skill is explicitly categorized as AI Native in Core Responsibilities 1) Modern BW/4HANA Data Warehouse Engineering Design and develop BW/4HANA models aligned to data warehousing and operational reporting needs. Build on BW/4HANA principles as a modern packaged data warehousing solution that is highly optimized for SAP HANA and runs only on SAP HANA DB. 2) AI Native Analytics Experiences (Grounded + Governed) Build LLM-powered experiences that answer business questions using retrieval + grounding over BW/4HANA models (e.g., queryable semantic artifacts), ensuring responses remain traceable to governed enterprise definitions. (Design intent / AI native pattern — not a claim about a specific SAP feature.) Implement analytics agents that can interpret intent, retrieve the right context from modeled data, and provide outputs with controlled safety boundaries. (Design intent.) 3) Performance, Optimization & Multi Temperature Data Management Engineer for enterprise-scale performance by applying optimization techniques and patterns relevant to BW on HANA and BW/4HANA. Incorporate Data Tiering Optimization (DTO) concepts to manage hot/warm/cold data tiers where applicable. 4) Data Modeling, ETL/ELT & Integration Discipline Deliver robust pipelines and transformations using BW on HANA / BW/4HANA data modeling and ETL concepts (including extraction/loading and reporting enablement as part of the warehouse lifecycle). Ensure cross-system consistency and harmonization as part of the warehouse s role in the broader data architecture. 5) Evaluation, Reliability & Observability for AI Behaviors Define evaluation loops (offline test sets + online monitoring) for AI-native analytics so changes to prompts/retrieval don t silently degrade accuracy. (Design intent.) Add observability for AI interactions (grounding rate, failure modes, latency/cost) and build fallback behaviors for reliability. (Design intent.) 6) Rapid Prototyping Production Scale-Out