Principal Product Manager, Edge AI, LLM & Agentic Workflows

HP•Palo Alto, CA

About The Position

The Principal Product Manager defines product strategy and drives delivery of Edge AI solutions for large language models (LLMs) and agentic workflows, including inference software capabilities for deploying, orchestrating, and operating these workloads in enterprise environments. Within HP’s Edge & AI solutions organization, the role owns the vision, roadmap, requirements, and product outcomes for assigned LLM and agentic inference capabilities. It partners 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 position combines strategic product leadership with practical execution through customer discovery, technical requirements, prioritization, and deployment validation. It translates customer needs into capabilities that help organizations deploy, manage, and scale AI closer to their users, operations, and data. The role defines software capabilities and integration requirements for LLM and agentic inference across local, on-premises, and hybrid environments. It establishes requirements for model serving and inference software, agent orchestration, enterprise controls, and model lifecycle management. It also partners on delivery, customer and partner pilots, go-to-market planning, and the measurement of product adoption and outcomes. The position requires 10+ years of relevant experience, including substantial product management responsibility for enterprise software, AI software, developer tools, or infrastructure products. A bachelor’s degree in Computer Science, Engineering, Data Science, or a related field—or equivalent relevant professional experience—is required. An MBA or advanced degree in a relevant technical field is a plus but not required. The role is full-time. The U.S. annual pay range is $147,050 to $230,850, with additional opportunities for bonus and/or equity. HP states that pay varies by work location, job-related knowledge, skills, and experience.

Requirements

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, and 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.
Bachelor’s degree in Computer Science, Engineering, Data Science, or a related field, or equivalent relevant professional experience.

Nice To Haves

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.
An MBA or an advanced degree in a relevant technical field.

Responsibilities

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.
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 solutions meet workload and customer requirements.
Establish requirements for model serving and assigned inference software capabilities, including deployment, runtime compatibility, scaling, relevant inference backends, and integration with HP Z Runtime where applicable.
Work with engineering and hardware partners to evaluate workload trade-offs involving 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.
Partner with product managers, engineering, and operations to translate the roadmap into prioritized requirements, acceptance criteria, and coordinated delivery plans.
Engage customers, ISVs, and ecosystem partners to validate LLM and agentic workflows and guide pilots toward reliable, supportable production deployments.
Use relevant Customer Zero initiatives as a source of deployment evidence and product feedback.
Evaluate build, buy, and partner options across the solution stack, establishing integration requirements and responsibilities with relevant teams.
Partner with product marketing and sales to define target customers, articulate differentiated value, inform pricing and packaging, 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.