Interested in this AI/ML Engineer role at Hewlett Packard Enterprise | HPE?
Apply Now →Skills & Technologies
About This Role
AI Value Architect - Private Cloud AI
This role has been designed as 'Hybrid' with a requirement 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:
As an AI Value Architect for North America, you will own the Day-2 value, adoption and expansion lifecycle for a portfolio of strategic HPE Private Cloud AI customers. You will act as a trusted technical advisor and AI value partner after initial deployment, helping customers turn early onboarding outcomes into broader internal adoption, stronger champions, clearer AI roadmaps and measurable business impact. You will combine deep technical credibility in enterprise AI and Generative AI with confident, commercial judgment: identifying where customers are stuck, what they need next, and where additional Private Cloud AI capacity, capabilities or services create the next step in their AI journey.
You will be North America aligned and customer-present while operating as part of a globally coordinated team. This is a customer-facing technical sales role, not a delivery or consulting role: you will help customers understand what is possible with Private Cloud AI, review progress against their roadmap, uncover blockers, identify new internal use cases and champions, and work closely with Sales, Pre-Sales, Forward Deployed Engineering, Product and Support to turn validated needs into clear next steps.
What You’ll Do
Continuous Value Creation
- Own a portfolio of strategic Private Cloud AI customers beyond initial onboarding and drive continued internal adoption, value realization and expansion of Private Cloud AI across business units.
- Discover and shape new AI, GenAI and agentic AI use cases across business units, moving customers from initial workloads to broader production adoption.
- Provide architecture guidance for new workloads across inference, RAG, AI agents, model serving, fine-tuning, data pipelines, observability, governance and security.
- Evangelize Private Cloud AI inside the customer organization by enabling technical and business champions, sharing relevant roadmap updates, and helping teams understand where Private Cloud AI can support their next AI initiatives.
Structured Adoption Cadence
- Establish a programmatic customer cadence with quarterly value, adoption and roadmap reviews involving customer stakeholders and relevant HPE account teams.
- Run outcome-focused business and technical reviews that ask practical adoption questions: what was achieved since onboarding, how much of the roadmap has progressed, where the customer is stuck, what blockers exist, and what technical or commercial actions are needed next.
- Plan and lead regular onsite engagements at strategic customers; travel frequently within North America as a core part of the role and selectively worldwide.
- Maintain clear account plans, actions, risks, value hypotheses and expansion signals across the portfolio.
Expansion & Proof
- Identify, qualify and technically shape expansion opportunities by recognizing where customers need additional Private Cloud AI capacity, new capabilities, platform expansion or related services to execute their AI roadmap.
- Run targeted roadmap, architecture, scaling and adoption workshops when expansion triggers emerge, focused on clarifying the customer’s next AI priorities and required Private Cloud AI capabilities.
- Build the technical and commercial value case for expansion, connect it to roadmap progress and measurable outcomes, and hand validated opportunities to Sales with clear scope, customer evidence and next-step recommendations.
- Partner with local Sales, Pre-Sales and Partners through opportunity progression while remaining a trusted technical and commercial advisor who is comfortable discussing investment needs, expansion options and customer outcomes.
Build the Global Playbook
- Design customer workshops, discovery guides, assessment tools, reference architectures and proof patterns that can be reused across customer sites and North America.
- Create the operating model, templates, metrics and governance for a new global AI value and expansion motion.
- Capture field learnings, customer roadmap signals, adoption blockers and expansion patterns, feed them back to Product and global teams, and continuously improve the playbook.
- Operate effectively in a build-from-scratch, startup-style environment with high ownership, ambiguity and pace.
How Success Will Be Measured
- Expansion creation: qualified, sales-accepted opportunities for additional Private Cloud AI systems and related services.
- Program scale: reusable workshops, assets and operating practices adopted across the global team.
- Customer trust: sustained executive and practitioner engagement with strategic accounts.
- Customer value: additional use cases activated, stronger internal champions, measurable roadmap progress, broader adoption across business units, stronger customer self-sufficiency and increased platform utilization.
What You Need to Bring
Technical & AI Expertise
- Strong hands-on knowledge of enterprise AI and GenAI architectures, including LLM inference, RAG, vector databases, AI agents, model endpoints, evaluation, guardrails and LLMOps/MLOps.
- Familiarity and first experiences with agent harness such as OpenClaw, NemoClaw, Hermes and OpenCode.
- Experience designing or implementing production AI solutions using Python, Linux, containers, Kubernetes, Helm, APIs, identity and access controls, and object/file storage.
- Knowledge of data engineering and ML platforms such as Airflow, Kubeflow, MLflow, Ray, model serving frameworks and observability tooling.
- Working knowledge around GPUs, Power Cooling and Networking.
- Ability to reason end-to-end - from customer outcome and data readiness through application architecture, platform sizing, security, operations and scale.
- Experience with NVIDIA AI Enterprise technologies, HPE Private Cloud AI, or comparable enterprise AI platforms is strongly preferred.
Customer & Commercial Skills
- Demonstrated success in a customer-facing technical sales or advisory role such as AI Solutions Architect, Value Architect, Pre-Sales Engineer, Forward Deployed Engineer, Customer Engineer or Field CTO-style role.
- Ability to translate complex AI technology into business value, facilitate workshops with technical and executive stakeholders, and influence without direct authority.
- Strong commercial curiosity and judgment: able to recognize expansion signals, ask direct investment-oriented questions, build a credible technical value case and collaborate with Sales without compromising trusted-advisor status.
- Strong program and account management skills across multiple strategic customers, with disciplined follow-through and concise executive communication.
- Excellent written and verbal communication skills in English; additional languages are an advantage.
Ways of Working
- Builder mentality: comfortable creating a new motion, assets and processes rather than inheriting a mature playbook.
- High ownership, bias for action and comfort operating in ambiguity.
- Collaborative and low-ego, with the ability to work across North America and global teams.
- Willingness and ability to travel frequently for onsite customer engagements across North America; frequent North America travel is required, with occasional international travel.
Preferred Background
- Significant experience delivering enterprise AI/ML, data-platform, cloud-native or GenAI solutions in customer environments.
- Experience working with strategic enterprise accounts and navigating both engineering and commercial stakeholders.
- Experience developing and delivering repeatable workshops, solution accelerators, reference architectures or field enablement assets.
Accessibility
HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form linked here.
Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.
What We Can Offer You:
Health & Wellbeing
We strive to provide our team members and their loved ones with a comprehensive suite of benefits that supports their physical, financial and emotional wellbeing.
Personal & Professional Development
We also invest in your career because the better you are, the better we all are. We have specific programs catered to helping you reach any career goals you have — whether you want to become a knowledge expert in your field or apply your skills to another division.
Unconditional Inclusion
We are unconditionally inclusive in the way we work and celebrate individual uniqueness. 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.
Let's Stay Connected:
Follow @HPECareers on Instagram to see the latest on people, culture and tech at HPE.
#unitedstatesJob:
SalesJob Level:
TCP_03
"The expected salary/wage range for this position is provided below. Actual offer may vary from this range based upon geographic location, work experience, education/training, and/or skill level.
– United States of America: Annual Salary USD 120,500 - 284,000 in Texas
This range reflects the minimum to maximum combined base and target-level sales compensation that would be paid if the hire performs at 100% of their sales plan. Of that on-target pay amount, the mix of base salary and target-level sales compensation is 80%/20%." Information about employee benefits offered in the US can be found at https://myhperewards.com/main/new-hire-enrollment.html
HPE is an Equal Employment Opportunity/ Veterans/Disabled/LGBT employer. We do not discriminate on the basis of race, gender, or any other protected category, and all decisions we make are made on the basis of qualifications, merit, and business need. Our goal is to be one global team that is representative of our customers, in an inclusive environment where we can continue to innovate and grow together. Please click here: Equal Employment Opportunity.
Hewlett Packard Enterprise is EEO Protected Veteran/ Individual with Disabilities.
HPE will comply with all applicable laws related to employer use of arrest and conviction records, including laws requiring employers to consider for employment qualified applicants with criminal histories.
Recruitment Fraud Alert
We have become aware of an increase in fraudulent recruitment activities in which individuals impersonate our company or authorized recruitment agencies to offer fake employment opportunities. These scams may occur through false websites, emails, social media, or chat-based applications and often aim to obtain personal information or money. Please note that Hewlett Packard Enterprise (HPE), its direct and indirect subsidiaries and affiliated companies, and its authorized recruitment agencies/vendors will never charge a candidate a registration fee, hiring fee, or any other fee in connection with its recruitment and hiring process. We also never request personal information such as back account details, Social Security numbers, or national IDs via social media or chat applications.
All legitimate job opportunities will come through official company channels, and candidates are responsible for verifying the credentials of any third party claiming to represent the company. Any reliance on fraudulent communication is at the individual’s own risk, and HPE disclaims legal liability for any resulting damages. If you suspect recruitment fraud, do not share personal information or make any payments and report the incident to your local authorities immediately.
Salary Context
This $120K-$284K range is above the median for AI/ML Engineer roles in our dataset (median: $176K across 1540 roles with salary data).
View full AI/ML Engineer salary data →Role Details
About This Role
AI/ML Engineers build and deploy machine learning models in production. They work across the full ML lifecycle: data pipelines, model training, evaluation, and serving infrastructure. The role has evolved significantly over the past two years. Where ML Engineers once spent most of their time on model architecture, the job now tilts heavily toward inference optimization, cost management, and integrating LLM capabilities into existing systems. Companies want engineers who can ship production systems, and the experimenter-only role is fading fast.
Day-to-day, you're writing training pipelines, debugging data quality issues, setting up evaluation frameworks, and figuring out why your model performs differently in staging than it did on your dev set. The best ML engineers are obsessive about reproducibility and measurement. They instrument everything. They know that a model is only as good as the data feeding it and the infrastructure serving it.
Across the 9,653 AI roles we're tracking, AI/ML Engineer positions make up 76% of the market. At Hewlett Packard Enterprise | HPE, this role fits into their broader AI and engineering organization.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
What the Work Looks Like
A typical week might include: debugging a data pipeline that's silently dropping 3% of training examples, running A/B tests on a new model version, writing documentation for a feature flag system that lets you roll back model deployments, and reviewing a junior engineer's PR for a new evaluation metric. Meetings tend to be cross-functional since ML touches product, engineering, and data teams.
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
Skills Required
Python and PyTorch dominate the requirements. Most roles expect experience with cloud platforms (AWS, GCP, or Azure) and familiarity with ML frameworks like TensorFlow or JAX. RAG (Retrieval-Augmented Generation) has become a top-3 skill requirement as companies integrate LLMs into their products. Docker and Kubernetes show up in about a third of postings, reflecting the production focus of the role.
Beyond the core stack, employers increasingly want experience with experiment tracking tools (MLflow, Weights & Biases), feature stores, and vector databases. Fine-tuning experience is valuable but less common than you'd think from reading Twitter. Most production LLM work is RAG and prompt engineering, not fine-tuning. If you have both, you're in a strong position.
Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
Compensation Benchmarks
AI/ML Engineer roles pay a median of $212,000 based on 11,857 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($202K) sits 5% below the category median. Disclosed range: $120K to $284K.
Across all AI roles, the market median is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. For comparison, the highest-paying categories include AI Safety ($253,100) and Research Engineer ($252,000). By seniority level: Entry: $110,408; Mid: $193,200; Senior: $228,700; Director: $272,275; VP: $235,000.
Hewlett Packard Enterprise | HPE AI Hiring
Hewlett Packard Enterprise | HPE has 5 open AI roles right now. They're hiring across AI/ML Engineer, Research Engineer. Positions span MD, US, Fort Collins, CO, US, SC, US. Compensation range: $252K - $652K.
Location Context
Across all AI roles, 6% (568 positions) offer remote work, while 9,010 require on-site attendance. Top AI hiring metros: New York (2,878 roles, $221,000 median); San Francisco (2,413 roles, $264,500 median); Los Angeles (1,340 roles, $210,000 median).
Career Path
Common paths into AI/ML Engineer roles include Data Scientist, Software Engineer, Research Engineer.
From here, career progression typically leads toward ML Architect, AI Engineering Manager, Principal ML Engineer.
The fastest path into ML engineering is through software engineering with a self-directed ML education. A CS degree helps, but production engineering skills matter more than academic credentials. Build something that works, deploy it, and measure it. That portfolio project is worth more than a Coursera certificate. For career growth, the fork comes around the senior level: go deep on technical complexity (staff/principal track) or move into managing ML teams.
What to Expect in Interviews
Expect system design questions around ML pipelines: how you'd build a training pipeline for a specific use case, handle data drift, or design A/B testing infrastructure for model deployments. Coding rounds typically involve Python, with emphasis on data manipulation (pandas, numpy) and algorithm implementation. Take-home assignments often ask you to build an end-to-end ML pipeline from raw data to deployed model.
When evaluating opportunities: Companies that are serious about AI/ML hiring tend to post specific infrastructure details in the job description: the frameworks they use, their model serving stack, their data pipeline tools. Vague postings that just say 'ML experience required' without specifics are often companies that haven't figured out what they need yet.
AI Hiring Overview
The AI job market has 9,653 open positions tracked in our dataset. By seniority: 193 entry-level, 4,500 mid-level, 3,455 senior, and 1,505 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (568 positions). The remaining 9,010 roles require on-site or hybrid attendance.
The market median for AI roles is $213,400. Top-quartile compensation starts at $264,230. The 90th percentile reaches $319,400. Highest-paying categories: AI Safety ($253,100 median, 57 roles); Research Engineer ($252,000 median, 419 roles); AI Engineering Manager ($251,622 median, 51 roles).
Demand for AI/ML Engineers has been strong and consistent. Unlike some AI roles that spike with hype cycles, ML engineering is a foundational need. Every company deploying AI models needs people who can keep them running, and the gap between research prototypes and production systems keeps growing.
The AI Job Market Today
The AI job market spans 9,653 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (7,301), Data Scientist (441), AI Software Engineer (438). 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 (193) are outnumbered by mid-level (4,500) and senior (3,455) 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 1,505 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (568 positions), with 9,010 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 $213,400. Top-quartile roles start at $264,230, and the 90th percentile reaches $319,400. 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 $253,100 median, while Prompt Engineer roles sit at $141,440. 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 (1,605 postings), Aws (916 postings), Azure (724 postings), Rag (634 postings), Gcp (522 postings), Pytorch (495 postings), Kubernetes (384 postings), Claude (383 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
Get Weekly AI Career Intelligence
Salary data, skills demand, and market signals from 16,000+ AI job postings. Every Monday.