Principal Product Manager, Edge AI, LLM & Agentic Workflows

Principal Product Manager, Edge AI, LLM & Agentic Workflows Description - Role Overview The Principal Product Manager defines product strategy and drives delivery of Edge AI solutions for large language models (LLMs) and agentic workflows, including the inference software capabilities needed to deploy, orchestrate, and operate these workloads in enterprise environments. Within HP’s Edge & AI solutions organization, this role owns the vision, roadmap, requirements, and product outcomes for assigned LLM and agentic inference capabilities. You will partner with engineering, peer product managers, hardware teams, services teams, and ecosystem partners to deliver integrated solutions. The role defines workload requirements and evaluates deployment trade-offs with hardware partners but does not own hardware products or hardware roadmaps. The role combines strategic product leadership with practical execution through customer discovery, technical requirements, prioritization, and deployment validation. You will translate internal and external customer needs into capabilities that help organizations deploy, manage, and scale AI closer to their users, operations, and data. Key responsibilities: Product Strategy & Customer Discovery Define the product vision, strategy, and roadmap for assigned LLM and agentic inference capabilities, aligned with HP’s broader Edge AI strategy. Coordinate shared capabilities and dependencies with peer product managers. Use customer discovery, market insights, competitive analysis, and business cases to prioritize enterprise opportunities for LLM applications, AI agents, and workflow automation. Validate customer needs, target users, deployment requirements, and expected business value before committing to solution investments. Solutions Definition Define software capabilities and integration requirements for LLM and agentic inference solutions across local, on-premises, and hybrid environments. Partner with hardware and services owners to ensure the overall solution meets workload and customer requirements. Establish requirements for model serving and assigned inference software stack capabilities, including deployment, runtime compatibility, scaling, support for relevant inference backends, and integration with HP Z Runtime where applicable. Work with engineering and hardware partners to evaluate workload requirements and trade-offs across compute, memory, acceleration, connectivity, performance, and cost. Define requirements for agent orchestration, tool use, enterprise system integration, human oversight, and recovery from failed or incomplete tasks. Translate model lifecycle, security, privacy, and governance needs into product requirements, including evaluation, deployment, monitoring, versioning, updates, rollback, access controls, and auditability. Delivery & Ecosystem Partnerships Partner with peer product managers, engineering, and operations to translate the roadmap into prioritized requirements, acceptance criteria, and coordinated delivery plans. Engage internal and external customers, independent software vendors (ISVs), and ecosystem partners to validate LLM and agentic workflows and guide pilots toward reliable, supportable production deployments. Use relevant Customer Zero initiatives as one source of deployment evidence and product feedback. Evaluate build, buy, and partner options across the solution stack, establishing clear integration requirements and responsibilities with relevant teams. Go-to-Market & Product Outcomes Partner with product marketing and sales to define target customers, articulate differentiated value, inform pricing and packaging decisions, and develop launch plans and sales enablement materials. Define and track adoption, time to production, workflow effectiveness, reliability, usability, and customer value. Use customer feedback and product performance evidence to refine priorities and improve the solution throughout its lifecycle. Core Outputs & Artifacts Product Strategy and Roadmap: A clear view of target customers, priority LLM and agentic workflows, differentiated value, and planned inference software capabilities, including dependencies on shared components, hardware, services, and partners. Prioritized Product Requirements: Requirements and acceptance criteria covering assigned inference software capabilities, enterprise controls, integration interfaces, and deployment needs, with workload requirements communicated to hardware and services owners. Validated Customer and Deployment Findings: Evidence from discovery, pilots, and production use that informs product decisions, including success criteria and barriers to adoption. Launch and Production Readiness Inputs: Product inputs covering integration dependencies, deployment and support requirements, partner responsibilities, positioning, and measures of success. What Success Looks Like A focused roadmap connects enterprise customer needs with HP’s Edge AI strategy and integrated solution capabilities. Customers move from AI experimentation and pilots into production deployments with clear operational and business value. Assigned inference software capabilities enable reliable, manageable LLM applications and agentic workflows in the intended deployment environments. Engineering, Go-to-Market, operations, and ecosystem partners share clear priorities, acceptance criteria, and delivery responsibilities. Product decisions are informed by adoption, customer feedback, task success, reliability, inference latency, and deployment cost, using measures appropriate to each solution. Experience knowledge & skills: Required 10+ years of relevant experience, including substantial product management responsibility for enterprise software, AI software, developer tools, or infrastructure products, with demonstrated ownership of strategy, roadmaps, and delivery. Demonstrated experience delivering LLM-enabled applications or inference solutions, with technical fluency in model serving, retrieval-augmented generation, agent orchestration, tool use, evaluation, and failure handling. Experience defining requirements for model deployment and lifecycle management, including monitoring, versioning, updates, and supportability in enterprise environments. Understanding of local, on-premises, and hybrid deployment constraints, with the ability to translate workload needs into requirements for engineering and hardware partners. Working fluency with Python-based AI ecosystems, APIs, SDKs, and developer tooling, sufficient to evaluate integration and deployment approaches alongside engineering. Experience translating enterprise integration, security, privacy, and governance needs into product requirements. Experience collaborating with technology partners and peer product managers to deliver integrated solutions and manage shared dependencies. Strong analytical judgment and experience using customer feedback, deployment findings, and performance evidence to prioritize product improvements. Demonstrated ability to influence cross-functional teams without direct authority, communicate with technical and executive audiences, and translate strategy into prioritized requirements and acceptance criteria. Preferred Experience evaluating build, buy, and partner options for enterprise platforms or integrated technology solutions. Experience with open-source AI ecosystems and enterprise model-serving or agent-orchestration platforms. Experience supporting product positioning, pricing and packaging decisions, and Go-to-Market execution for enterprise technology offerings. Education: Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent relevant professional experience. An MBA or an advanced degree in a relevant technical field is a plus but not required. Disclaimer • This job description describes the general nature and level of work performed in this role. It is not intended to be an exhaustive list of all duties, skil