Senior Agentic AI Systems Engineer – Runtime Frameworks & Resource Intelligence

About HP At HP, you’ll have a chance to create tools, technology, and solutions that reshape the way the world works in the future. If you’re looking to join a company that allows you to connect with a network of professionals eager to support you in doing your best work, we want to talk to you. Our legendary culture guides every employee toward success—fostering collaboration and driving innovation. Operating in over 170 countries, HP is always creating new services, products, and capabilities giving you more opportunities to advance your career. Here, innovation is the key to professional development and career mobility. When you join HP, you’re joining a company that believes every voice matters and that we all deserve a seat at the table. From the boardroom to factory floor, we create a culture where everyone is respected and where people can be themselves. You will be part of a global laboratory where different perspectives and experiences will help you solve problems in new ways. This is where you can build a long and wide-ranging career. The Commercial Systems Software Engineering organization is developing next-generation AI software platforms that enable intelligent, secure, and scalable agentic experiences across client and edge computing environments. We are seeking an experienced Senior Agentic AI Systems Engineer to analyze, tailor, and integrate agentic AI frameworks for environments where multiple agents operate concurrently under changing system conditions. The role will focus on the runtime frameworks, resource-management policies, system services, and platform interfaces required to deliver responsive and reliable agentic experiences across diverse PC and edge platforms. The successful candidate will combine knowledge of agentic AI frameworks with strong systems software, performance engineering, memory management, and heterogeneous computing experience. The individual will evaluate how agentic workloads interact with CPU, GPU, NPU, memory, storage, network, battery, power, and thermal resources, and develop adaptive capabilities that enable effective execution within available system constraints. Responsibilities • Analyze agentic AI frameworks and runtime architectures for deployment across PC and edge computing environments. • Tailor and integrate agentic frameworks to operate effectively under changing resource and system conditions. • Evaluate how multiple concurrent agents, AI models, and applications use and compete for compute, memory, storage, networking, power, and thermal resources. • Develop policies for workload admission, prioritization, scheduling, orchestration, throttling, suspension, recovery, and termination. • Create capabilities that maintain application and system responsiveness while multiple agentic workloads execute concurrently. • Develop graceful-degradation strategies for memory pressure, compute contention, low battery, thermal limits, intermittent connectivity, and other constrained conditions. • Analyze the system impact of agent planning, tool execution, retrieval, model inference, context management, state persistence, and inter-agent communication. • Define approaches for managing agent context, memory, state, shared resources, and long-running sessions. • Develop abstractions that support different AI models, runtimes, hardware configurations, and local, edge, or cloud-connected execution environments. • Create workload characterization, benchmarking, stress-testing, and validation methods for multi-agent scenarios. • Instrument agentic runtimes to collect telemetry related to performance, resource consumption, responsiveness, reliability, and execution outcomes. • Diagnose complex issues spanning agentic frameworks, AI runtimes, operating systems, applications, services, and hardware resources. • Develop reusable runtime services, software components, APIs, libraries, tools, and platform integration guidance. • Collaborate with architects, AI engineers, systems engineers, application teams, quality teams, and technology partners to deliver production-ready capabilities. • Apply secure development, automated testing, observability, CI/CD, and operational best practices throughout the software lifecycle. Education & Experience Recommended • Four-year or graduate degree in Computer Science, Software Engineering, Computer Engineering, Electrical Engineering, or a related discipline, or equivalent experience. • 8+ years of experience in systems software, AI infrastructure, runtime engineering, performance engineering, distributed systems, or a related field. • Demonstrated experience evaluating, integrating, or adapting AI frameworks, runtime systems, orchestration platforms, or complex software platforms. • Experience developing performance-sensitive software for client, edge, embedded, or other resource-constrained environments. • Experience analyzing system-level performance, scalability, reliability, and resource-contention challenges. • Proven ability to diagnose issues involving complex interactions across applications, operating systems, AI runtimes, and hardware. • Experience delivering software in collaboration with architecture, engineering, product, quality, and partner organizations. Required Technical Skills Agentic AI Frameworks and Runtime Systems • Strong understanding of agentic AI concepts, including planning, reasoning, memory, retrieval, tool use, state management, workflow orchestration, and multi-agent coordination. • Experience with agentic frameworks and orchestration technologies such as Semantic Kernel, AutoGen, LangGraph, LangChain, LlamaIndex, or comparable platforms. • Understanding of sequential, parallel, event-driven, hierarchical, and long-running agent execution patterns. • Familiarity with tool integration, agent communication, shared services, workflow state, and agent lifecycle management. • Understanding of how planning loops, model calls,