AI/ML Engineer - Agentic

AI/ML Engineer - Agentic This role has been designed as ‘Hybrid’ with an expectation that you will work on average 2 days per week from an HPE office. Who We Are: Hewlett Packard Enterprise is the global edge-to-cloud company advancing the way people live and work. We help companies connect, protect, analyze, and act on their data and applications wherever they live, from edge to cloud, so they can turn insights into outcomes at the speed required to thrive in today’s complex world. Our culture thrives on finding new and better ways to accelerate what’s next. We know varied backgrounds are valued and succeed here. We have the flexibility to manage our work and personal needs. We make bold moves, together, and are a force for good. If you are looking to stretch and grow your career our culture will embrace you. Open up opportunities with HPE. Job Description: Job Definition: The AI/ML Engineer – Agentic is a senior individual contributor responsible for designing, building, and operating a production-grade agentic orchestration platform, including multi-agent workflows and MCP server–based tool infrastructure. The role focuses on enterprise-scale LLM integration, shared retrieval and memory services, and high‑performance backend systems that power agent execution. This position owns reliability, observability, and cloud-native operations for non-deterministic agentic syste [... source excerpt omitted ...] provide assistance to lower level professionals. Exercises independent judgment and consults with others to determine best method for accomplishing work and achieving objectives. Responsibilities: Design, build, and own a production-grade agentic orchestration platform, implementing scalable multi-agent workflows using frameworks such as LangGraph or equivalent. Architect, develop, and operate the MCP server infrastructure, including inter-agent communication, tool/server registries, domain isolation, versioning, and lifecycle management. Integrate and operate LLM services at enterprise scale, supporting streaming, structured outputs, tool/function calling, and robust error handli [... source excerpt omitted ...] ering, information systems, or closely related quantitative discipline. Master’s desirable. Typically, 4-7 years’ experience. Knowledge and Skills: Core Agentic/Orchestration: Production experience with agentic frameworks: LangGraph (preferred), Claude Agent SDK, or equivalent (not just prototypes) Deep understanding of multi-agent architectures: supervisor/worker patterns, hierarchical agent graphs, ReAct loops, ReWoo Hands-on with inter-agent communication protocols: MCP (Model Context Protocol), A2A, tool registry / server registry LLM & ML Engineering: LLM API integration at scale: structured outputs, streaming, function/tool calling, error handling RAG pipeline design