Join us as a Custom Software Engineer focused on Packaged Application Development. This full-time role requires 2-5 years of experience and involves designing, coding, and enhancing components across various systems and applications. You will utilize modern frameworks and agile practices to deliver scalable, high-performing solutions tailored to specific business needs.

This position emphasizes the strategic application of AI-assisted development tools. You'll leverage AI copilots to enhance requirement analysis, configuration, testing, documentation, and problem resolution within SAP Materials Management (MM). The goal is to streamline procurement, inventory management, and supplier collaboration, integrating pre-built AI capabilities to boost productivity without requiring deep expertise in core AI development.

Key responsibilities include configuring and optimizing SAP MM processes such as Procure to Pay (P2P), Purchase Requisition/Order Management, Vendor Management, and Inventory Management. You will use AI copilots to generate functional specifications, validate configuration logic, and create high-quality test cases. Support will be provided for end-to-end delivery, including requirements gathering, solution design, and hypercare.

Apply AI tools to accelerate requirement interpretation, process design, and error diagnostics in procurement and inventory workflows. Integrate pre-built AI services to enhance MM operations, like automated classification of materials or predictive signals for low stock. Utilize LLM-assisted troubleshooting to reduce incident resolution time. Ensure compliance with procurement governance and audit policies, collaborating with various teams to deliver cohesive MM solutions.

A strong understanding of SAP MM processes, including P2P, purchasing document flows, vendor evaluation, and inventory management is essential. Experience configuring material master, vendor master, PO/PR processing, and release strategies is required. Familiarity with integration across SD, FI, PP, and QM, as well as knowledge of IDocs and common MM troubleshooting patterns, is necessary.

Demonstrated hands-on experience using AI copilots, LLM-powered documentation tools, and AI-assisted testing accelerators is crucial. You should be able to integrate pre-built AI capabilities into MM workflows for automation and insight generation. A continuous learning mindset and comfort adopting AI-enabled productivity tools are highly valued.