**Key Responsibilities** **1. Core Logistics Data Integration** ● **SAP Connectivity:** Design and implement robust integration flows to extract and ingest data the MuleSoft SAP Connector (RFC, BAPI, IDoc) for both synchronous and asynchronous transactions. ● **Legacy Data Transformation:** Develop complex **DataWeave 2.0** scripts to translate, cleanse, and canonicalize data between DoD logistics standards and internal JSON/XML formats. ● **Reliability & Resilience:** Implement advanced error handling, queue-based patterns (e.g., Anypoint MQ Store and Forward), and guaranteed delivery mechanisms to ensure no mission-critical requisition or inventory update is ever lost. ● **Testing & Quality:** Write comprehensive **MUnit** test suites to achieve mandated code coverage (90%+) and validate complex business logic against DoD process rules. **2. Agentic AI Data Serving & Protocol ** ● **Model Context Protocol (MCP) Server Development:** Design and build specialized MuleSoft APIs (acting as "MCP Servers") optimized to serve contextual data directly to Agentic AI systems and RAG pipelines. ● **Vector & RAG Database Access:** Implement connectivity patterns to secure Vector Databases and other specialized data stores that house indexed, vectorized logistics information. ● **Efficient Data Retrieval:** Tune APIs for ultra-low-latency data retrieval necessary for AI models, focusing on minimizing payload size and optimizing database queries (SQL, NoSQL, or Graph) originating from the Mule flow. ● **Security for AI:** Enforce strict access control, tokenization, and data masking policies within the API layer. ● **Metadata Management:** Ensure that all served data includes rich, accurate metadata (timestamps, source system, confidence score) to improve the grounding and auditability of AI-generated responses.