Seeking a Custom Software Engineer to design, code, and enhance system components. This role leverages modern frameworks and agile practices to deliver scalable, high-performing solutions. The primary focus is on developing custom software tailored to specific business needs.

This position involves designing, configuring, and enhancing SAP Materials Management (MM) processes. You will utilize AI-assisted development tools and copilots to streamline requirement analysis, configuration, testing, documentation, and problem resolution. The goal is to ensure efficient procurement, inventory management, and supplier collaboration, enhanced by pre-built AI capabilities.

Configure and optimize SAP MM processes, including Procure-to-Pay (P2P), Purchase Requisition/Order Management, Source List & Quota Arrangements, Vendor Management, Inventory Management, Material Master Data, and Release Strategies. Utilize AI copilots for generating functional specifications, validating configuration logic, creating test cases, and automating documentation. Support end-to-end delivery from requirements gathering to hypercare, integrating MM with other SAP modules (SD, FI, PP, QM) and external systems. Apply AI tools to accelerate requirement interpretation, data validation, error diagnostics, test creation, and document generation. Integrate pre-built AI services for enhanced MM operations, such as automated classification and predictive signals. Employ LLM-assisted troubleshooting for faster incident resolution.

Must possess strong SAP MM Materials Management expertise, including in-depth knowledge of P2P processes, purchasing document flows, vendor evaluation, and inventory management. Experience configuring material master, vendor master, PO/PR processing, goods movements, and release strategies is essential. Familiarity with integration across SD, FI, PP, and QM is required, along with knowledge of IDocs, outputs, pricing procedures, and common MM troubleshooting patterns. Hands-on experience with AI copilots, LLM-powered documentation tools, and AI-assisted testing accelerators is necessary. Ability to integrate pre-built AI capabilities into MM workflows is key. Understanding of responsible AI usage and its limitations is also important. A minimum of 2-5 years of relevant experience and 15 years of full-time education are required. Strong analytical, problem-solving, communication, and collaboration skills are expected, along with a continuous learning mindset.