Interested in this AI/ML Engineer role at Hewlett Packard Enterprise | HPE?
Apply Now →Skills & Technologies
About This Role
Private Cloud AI Sales Specialist
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:
The Private Cloud AI Sales Specialist is responsible for driving growth of HPE’s AI business across New England, the Mid-Atlantic, and the Carolinas. This is a highly visible, customer-facing sales role focused on helping organizations move from AI experimentation to production by delivering secure, scalable, enterprise-ready AI infrastructure and platforms.
This individual will serve as the AI sales specialist across the territory, partnering closely with Account Managers, technical specialists, services teams, partners, and executive leadership to identify, develop, and close strategic AI opportunities.
The ideal candidate is a hunter and business builder who is comfortable creating opportunities from the ground up, engaging C-level executives and technical leaders, and navigating complex enterprise sales cycles. They should understand how AI is changing enterprise infrastructure requirements and be able to connect customer initiatives around generative AI, agentic AI, machine learning, data science, and accelerated computing to measurable business outcomes.
This role requires someone who can operate at the intersection of AI, infrastructure, hybrid cloud, software, and services while building trusted relationships with customers ranging from technical practitioners to CIOs, CTOs, CDOs, and business executives.
Responsibilities:
- Own and drive the Private Cloud AI sales pipeline across New England, the Mid-Atlantic, and the Carolinas, with responsibility for pipeline creation, opportunity development, forecasting, and revenue attainment.
- Proactively identify and develop net-new AI opportunities through prospecting, account planning, executive engagement, workshops, customer events, partner activities, and targeted sales campaigns.
- Partner closely with Account Managers and extended sales teams to develop AI strategies within existing accounts while also identifying and landing new customers.
- Lead customer conversations around Generative AI, agentic AI, machine learning, AI infrastructure, accelerated computing, and private/hybrid AI architectures.
- Help customers evaluate where AI workloads should run across private cloud, public cloud, colocation, and hybrid environments based on performance, security, governance, data gravity, and economics.
- Develop a strong understanding of customer AI initiatives, use cases, data environments, application requirements, and business priorities to create differentiated solutions.
- Engage with customers from technical practitioners through CIO, CTO, CDO, infrastructure, data, AI/ML, security, and business leadership.
- Translate technical capabilities into clear business outcomes including faster time to value, improved developer productivity, greater control of sensitive data, predictable AI economics, and lower total cost of ownership.
- Lead complex sales pursuits from initial discovery through technical validation, business-case development, solution design, commercial negotiation, and close.
- Coordinate internal resources including AI architects, solution engineers, services, finance, product teams, and executive sponsors to advance strategic opportunities.
- Work with customers to develop AI business cases and cloud-versus-private-AI economic models, including infrastructure utilization, GPU consumption, token economics, and workload growth.
- Drive customer workshops, demonstrations, proof-of-concepts, hosted trials, benchmarks, and other technical validation activities when appropriate.
- Develop and maintain relationships with strategic partners across the AI ecosystem including NVIDIA, software vendors, ISVs, systems integrators, cloud providers, and channel partners.
- Maintain strong competitive awareness across AI infrastructure, GPU platforms, AI software, public cloud AI services, and emerging AI platforms.
- Accurately manage pipeline, opportunity progression, forecasting, and sales activity within the company's CRM and sales processes.
- Maintain current knowledge of rapidly evolving AI technologies, models, architectures, and market trends and translate those developments into actionable customer conversations.
- Act as an AI subject-matter expert and thought leader for customers, partners, and internal sales teams across the assigned territory.
Education and Experience Required:
- Bachelor's degree or equivalent directly related professional experience.
- Typically 8+ years of enterprise technology sales experience, preferably involving complex infrastructure, cloud, software, data, AI, or platform solutions.
- Demonstrated history of meeting or exceeding sales quotas and successfully closing complex enterprise opportunities.
- Proven experience prospecting, hunting, and creating net-new business, rather than relying exclusively on existing pipeline.
- Experience working collaboratively with Enterprise Account Managers in a specialist or overlay sales model.
- Experience engaging senior executives and technical decision-makers within mid-market and large enterprise organizations.
- Experience selling AI, Cloud, hybrid cloud, infrastructure, platform, or as-a-service solutions.
- Experience developing business cases and positioning technology based on business value, economics, risk reduction, and strategic outcomes.
- Experience navigating complex, multi-stakeholder enterprise sales cycles.
Knowledge and Skills:
- Strong understanding of the rapidly evolving enterprise AI, Generative AI, and Agentic landscape.
- Understanding of AI infrastructure concepts including GPUs, accelerated computing, high-performance networking, storage, data platforms, and AI workload requirements.
- Familiarity with the AI/ML lifecycle including model development, training, fine-tuning, inference, deployment, monitoring, and governance.
- Working knowledge of MLOps, LLMOps, model serving, model routing/gateways, Kubernetes, containers, APIs, Tokenomics, and modern AI application architectures.
- Understanding of private cloud, public cloud, and hybrid-cloud architectures and the tradeoffs associated with performance, security, governance, sovereignty, and cost.
- Ability to discuss AI economics, including public-cloud consumption models, infrastructure utilization, GPU requirements, and the potential business case for private AI.
- Strong understanding of enterprise security, governance, data privacy, and compliance considerations associated with AI adoption.
- Knowledge of the competitive AI ecosystem, including infrastructure vendors, cloud providers, AI platforms, model providers, and emerging AI software companies.
- Ability to translate highly technical concepts into clear business value for executive audiences.
- Strong discovery, consultative selling, presentation, negotiation, and closing skills.
- Ability to build consensus across multiple customer stakeholders and manage complex sales pursuits.
- Strong account-planning, pipeline-management, and forecasting discipline.
- Ability to operate effectively in a fast-moving market where technologies, competitors, customer requirements, and business models are evolving rapidly.
- High level of curiosity and a demonstrated commitment to continuously learning about emerging AI technologies and their implications for enterprise customers.
What Success Looks Like
Success in this role means becoming a trusted AI advisor to customers and account teams while consistently creating new opportunities and converting strategic AI initiatives into revenue.
The strongest candidates will combine enterprise sales discipline, technical curiosity, executive presence, and a hunter mentality. They will be equally comfortable discussing an AI strategy with a CIO, understanding desired business outcomes, working through GPU and platform requirements with an AI engineering team, building an economic justification with finance, and coordinating the resources necessary to move a complex opportunity from concept to production.
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.
Job:
SalesJob Level:
Expert
"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 225,500 - 437,500 in Massachusetts // 198,000 - 465,500 in District of Columbia & Maryland & North Carolina & Virginia
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 60%/40%." 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 $225K-$437K range is above the 75th percentile for AI/ML Engineer roles in our dataset (median: $178K across 1623 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 9,079 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 $211,950 based on 13,048 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $193,200. This role's midpoint ($331K) sits 56% above the category median. Disclosed range: $225K to $437K.
Across all AI roles, the market median is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. For comparison, the highest-paying categories include AI Safety ($256,000) and Research Engineer ($252,000). By seniority level: Entry: $111,000; Mid: $193,200; Senior: $229,300; Director: $272,300; VP: $235,830.
Hewlett Packard Enterprise | HPE AI Hiring
Hewlett Packard Enterprise | HPE has 10 open AI roles right now. They're hiring across AI/ML Engineer, Data Scientist, AI Software Engineer, Research Engineer. Positions span San Juan, PR, US, MD, US, Fort Collins, CO, US. Compensation range: $251K - $652K.
Location Context
Across all AI roles, 6% (531 positions) offer remote work, while 8,478 require on-site attendance. Top AI hiring metros: New York (3,078 roles, $221,000 median); San Francisco (2,646 roles, $261,000 median); Los Angeles (1,486 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 9,079 open positions tracked in our dataset. By seniority: 106 entry-level, 4,384 mid-level, 3,343 senior, and 1,246 leadership roles (Director, VP, C-Level). Remote roles make up 6% of the market (531 positions). The remaining 8,478 roles require on-site or hybrid attendance.
The market median for AI roles is $213,000. Top-quartile compensation starts at $263,911. The 90th percentile reaches $318,400. Highest-paying categories: AI Safety ($256,000 median, 66 roles); Research Engineer ($252,000 median, 457 roles); AI Engineering Manager ($251,622 median, 55 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 9,079 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (6,862), AI Product Manager (453), AI Software Engineer (451). 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 (106) are outnumbered by mid-level (4,384) and senior (3,343) 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 1,246 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 6% of all AI roles (531 positions), with 8,478 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 $213,000. Top-quartile roles start at $263,911, 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 $256,000 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 (1,649 postings), Aws (898 postings), Azure (723 postings), Rag (671 postings), Gcp (536 postings), Pytorch (434 postings), Claude (410 postings), Kubernetes (406 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.