Join our team as a Custom Software Engineer, focusing on Packaged Application Development. This full-time role involves designing, coding, and enhancing system components using modern frameworks and agile practices. You'll create scalable, high-performing solutions tailored to specific business needs, leveraging AI-assisted development tools to accelerate tasks like requirement analysis, configuration, testing, and problem resolution. This position is ideal for individuals with a strong background in SAP MM and a desire to integrate AI capabilities for enhanced productivity, without requiring deep involvement in AI model 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 utilize AI copilots to generate functional specifications, validate configuration, and automate documentation. Support will be provided across the entire delivery lifecycle, from requirements gathering to hypercare, and integration with other SAP modules and external systems. Furthermore, you'll apply AI tools to accelerate requirement interpretation, data validation, error diagnostics, test creation, and document generation, while also integrating pre-built AI services for enhanced MM operations like automated classification and predictive signals.

A strong understanding of SAP MM processes, including P2P, vendor evaluation, and inventory management, is essential. Experience in configuring material master, vendor master, PO/PR processing, and release strategies is required. Familiarity with integration across SD, FI, PP, and QM, along with knowledge of IDocs and common MM troubleshooting patterns, is necessary. Hands-on experience with AI copilots, LLM powered documentation tools, and AI-assisted testing accelerators is a must. You should be able to integrate pre-built AI capabilities into MM workflows and possess strong analytical, problem-solving, and communication skills. A continuous learning mindset and comfort with AI-enabled productivity tools are also important. Minimum 3 years of experience is required, along with 15 years of full-time education.