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About This Role
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.
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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.
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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.
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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, skills, responsibilities, knowledge, etc. These may be subject to change and additional functions may be assigned as needed by management.
Salary
The pay range for this role is $147,050 to $230,850 USD annually with additional opportunities for pay in the form of bonus and/or equity (applies to United States of America candidates only). Pay varies by work location, job-related knowledge, skills, and experience.
Benefits:
HP offers a comprehensive benefits package for this position, including:
- Health insurance
- Dental insurance
- Vision insurance
- Long term/short term disability insurance
- Employee assistance program
- Flexible spending account
- Life insurance
- Generous time off policies, including;
- 4-12 weeks fully paid parental leave based on tenure
- 11 paid holidays
Additional flexible paid vacation and sick leave ( US benefits overview ) *
The compensation and benefits information is accurate as of the date of this posting. The Company reserves the right to modify this information at any time, with or without notice, subject to applicable law.
Job -
Software Schedule -
Full time Shift -
No shift premium (United States of America) Travel -
Relocation -
Equal Opportunity Employer (EEO) -
HP, Inc. provides equal employment opportunity to all employees and prospective employees, without regard to race, color, religion, sex, national origin, ancestry, citizenship, sexual orientation, age, disability, or status as a protected veteran, marital status, familial status, physical or mental disability, medical condition, pregnancy, genetic predisposition or carrier status, uniformed service status, political affiliation or any other characteristic protected by applicable national, federal, state, and local law(s).
Please be assured that you will not be subject to any adverse treatment if you choose to disclose the information requested. This information is provided voluntarily. The information obtained will be kept in strict confidence.
For more information, review HP’s EEO Policy or read about your rights as an applicant under the law here: “ Know Your Rights: Workplace Discrimination is Illegal "
Salary Context
This $147K-$230K range is above the median for AI Product Manager roles in our dataset (median: $185K across 149 roles with salary data).
View full AI Product Manager salary data →Role Details
About This Role
AI Product Managers define what AI features get built and why. They translate business problems into ML-solvable tasks, work with engineering to scope model requirements, and own the metrics that determine if an AI feature is working. The role requires a rare combination of technical fluency and product instinct.
Unlike traditional product management, AI PM work involves managing uncertainty at a fundamental level. Your model might work 90% of the time. What happens the other 10%? What's the user experience when the AI is wrong? How do you measure 'good enough' for a probabilistic system? These questions don't have easy answers, and the AI PM is the person responsible for finding them.
Across the 10,827 AI roles we're tracking, AI Product Manager positions make up 4% of the market. At HP, this role fits into their broader AI and engineering organization.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
What the Work Looks Like
A typical week includes: reviewing model evaluation results with the ML team, defining success metrics for a new AI feature, conducting user research on how customers respond to AI-generated outputs, writing product requirements that include accuracy thresholds and fallback behaviors, and presenting the AI roadmap to leadership. You're the translator between technical capability and business value.
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
Skills Required
Technical fluency with ML concepts is essential, though you won't be writing models. Expect to understand training data, evaluation metrics, model limitations, and responsible AI practices. SQL and basic Python are increasingly expected. Experience with A/B testing, data analysis, and product analytics is baseline. Understanding LLM capabilities and limitations is now a core requirement.
The differentiator is AI-specific product thinking: knowing when to use ML vs. heuristics, understanding the cost of training data collection, designing graceful degradation for model failures, and building products that improve with usage data. Experience with AI safety, bias mitigation, and responsible AI deployment is increasingly important.
Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
Compensation Benchmarks
AI Product Manager roles pay a median of $220,350 based on 994 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000. This role's midpoint ($188K) sits 14% below the category median. Disclosed range: $147K to $230K.
Across all AI roles, the market median is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($266,050) and AI Engineering Manager ($253,461). By seniority level: Entry: $112,320; Mid: $193,200; Senior: $230,000; Director: $272,300; VP: $236,450.
HP AI Hiring
HP has 5 open AI roles right now. They're hiring across AI/ML Engineer, AI Product Manager, MLOps Engineer. Positions span Austin, TX, Palo Alto, CA, US, Fort Collins, CO, US. Compensation range: $161K - $230K.
Location Context
Across all AI roles, 6% (665 positions) offer remote work, while 10,097 require on-site attendance. Top AI hiring metros: New York (3,262 roles, $221,000 median); San Francisco (2,767 roles, $260,600 median); Los Angeles (1,585 roles, $210,000 median).
Career Path
Common paths into AI Product Manager roles include Product Manager, Data Analyst, Technical Program Manager.
From here, career progression typically leads toward Director of AI Product, VP Product, Head of AI.
The most effective path is PM experience plus self-directed AI education. Take Andrew Ng's courses, build a small ML project, and learn enough Python to read model evaluation code. The goal isn't to become an ML engineer. It's to have credibility in technical conversations and to understand what's possible, what's hard, and what's a bad idea.
What to Expect in Interviews
AI interviews typically combine coding challenges (Python-focused), system design questions tailored to the role, and discussions about your experience with relevant tools and frameworks. Strong candidates demonstrate both technical depth and the ability to make pragmatic engineering tradeoffs. Prepare portfolio projects that demonstrate end-to-end capability rather than isolated skills.
When evaluating opportunities: Strong postings describe specific AI products the PM will own, mention the ML team structure, and talk about measurement methodology. Look for companies that have already shipped AI features. Roles at companies that are 'exploring AI' often mean you'll spend a year defining the strategy before any building happens.
AI Hiring Overview
The AI job market has 10,827 open positions tracked in our dataset. By seniority: 122 entry-level, 5,256 mid-level, 3,922 senior, and 1,527 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (665 positions). The remaining 10,097 roles require on-site or hybrid attendance.
The market median for AI roles is $213,480. Top-quartile compensation starts at $264,111. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($266,050 median, 70 roles); AI Engineering Manager ($253,461 median, 58 roles); Research Engineer ($252,000 median, 476 roles).
AI Product Manager roles are growing as companies realize that shipping AI features requires different product thinking than traditional software. The best candidates combine product management experience with enough technical depth to have productive conversations with ML engineers about model capabilities and limitations.
The AI Job Market Today
The AI job market spans 10,827 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (8,288), AI Software Engineer (548), AI Product Manager (471). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (122) are outnumbered by mid-level (5,256) and senior (3,922) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 1,527 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (665 positions), with 10,097 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $213,480. Top-quartile roles start at $264,111, and the 90th percentile reaches $318,400. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $266,050 median, while Prompt Engineer roles sit at $140,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,093 postings), Aws (1,100 postings), Azure (910 postings), Rag (863 postings), Gcp (680 postings), Pytorch (549 postings), Claude (532 postings), Kubernetes (511 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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