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About This Role
Sales Enablement AI Architech
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:
Role Overview
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As a Sales Enablement AI Architect, you will design and scale AI\-powered enablement experiences that help sellers, sales engineers, partners, and the enablement team learn faster, sell smarter, and execute with greater consistency. This role sits at the intersection of sales enablement strategy, generative AI, platform architecture, content intelligence, coaching, analytics, and change management.
The Sales Enablement AI Architect will partner across a matrixed environment to move from experimentation to secure, measurable, scalable AI adoption that improves ramp time, seller productivity, content effectiveness, and revenue execution.
Key Responsibilities
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- Provide strategic direction for AI\-powered sales enablement, including generative AI, agentic workflows, content intelligence, coaching automation, and responsible AI adoption across go\-to\-market teams.
- Define the architecture for AI\-enabled seller experiences, including intelligent content discovery, guided selling, role\-based learning paths, sales playbooks, coaching agents, and workflow automation.
- Design agentic AI and retrieval\-augmented generation solutions that support opportunity preparation, account research, objection handling, competitive positioning, call planning, follow\-up generation, and trusted content discovery.
- Evaluate, design, and integrate AI solutions across the sales technology stack, including enablement platforms, CRM, learning systems, content management systems, conversation intelligence tools, and collaboration platforms.
- Develop governance models for AI\-generated enablement content, including source attribution, approval workflows, lifecycle management, brand consistency, compliance, and responsible AI controls.
- Create AI adoption strategies, pilots, and roadmaps that prioritize measurable outcomes such as ramp time, seller productivity, content usage, learning completion, pipeline velocity, and stakeholder satisfaction.
- Define evaluation frameworks and feedback loops to measure answer quality, content accuracy, seller adoption, workflow completion, time saved, coaching effectiveness, and business impact.
- Lead workshops, discovery sessions, architecture reviews, and executive presentations that explain AI capabilities, tradeoffs, risks, and adoption plans to technical and non\-technical audiences.
- Build prototypes, proofs of concept, and scalable implementation patterns for AI agents, prompt libraries, knowledge retrieval, workflow orchestration, and enablement analytics.
- Collaborate with platform owners and administrators to improve the seller experience across systems such as Seismic, Highspot, Intellum, LMS platforms, CRM, Microsoft 365, and other enablement or productivity tools.
- Mentor enablement, operations, and platform teams on AI literacy, prompt design, use\-case prioritization, experimentation practices, and responsible adoption.
Required Qualifications
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- 5\+ years of experience in solution architecture, AI strategy, AI execution, sales enablement, revenue enablement, sales operations, product marketing, learning technology, or a related go\-to\-market transformation role.
- Hands\-on experience designing or implementing AI\-powered workflows, generative AI use cases, intelligent assistants, automation, or data\-driven enablement solutions.
- Familiarity with generative AI concepts, including prompt engineering, retrieval\-augmented generation, AI agents, workflow orchestration, model evaluation, responsible AI, and human\-in\-the\-loop review.
- Understanding of sales enablement disciplines, including onboarding, everboarding, sales methodology, coaching, content strategy, playbooks, partner enablement, field readiness, and seller productivity.
- Experience working with enablement and go\-to\-market platforms such as Seismic, Highspot, Mindtickle, Salesforce, Microsoft 365, Teams, LMS platforms, content management systems, or conversation intelligence tools.
- Ability to translate seller, leader, and stakeholder needs into practical AI use cases, business requirements, solution designs, adoption plans, and measurable success criteria.
- Experience building governance models for enablement content, including accuracy, version control, approval processes, permissions, branding, localization, compliance, and lifecycle management.
- Strong analytical mindset with the ability to define and track metrics such as ramp time, content engagement, learning completion, seller adoption, productivity gains, pipeline impact, win\-rate influence, and program ROI.
- Strong facilitation and communication skills, with the ability to lead workshops, influence senior stakeholders, explain technical concepts clearly, and align cross\-functional teams around a shared roadmap.
- Ability to partner effectively with Sales, Sales Engineering, Product Marketing, Revenue Operations, Channel, IT, Security, Data, Legal, and AI governance teams.
Preferred Qualifications
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- Experience designing pilots, proofs of concept, change management plans, adoption campaigns, and feedback loops for new tools or enablement capabilities.
- Experience with enterprise AI governance, permission\-aware knowledge retrieval, AI risk management, AI\-powered coaching, guided selling, content recommendation, APIs, data integrations, analytics dashboards, workflow automation, or low\-code/no\-code AI development tools.
- Comfort operating in ambiguous environments, identifying high\-value opportunities, prioritizing use cases, and iterating quickly based on seller feedback and measurable outcomes.
*Knowledge and Skills:*
- Experience with Agentic AI
- Experience building AI Agents
- Superior business and financial planning acumen.
- Superior leadership, communication, change management, and project management skills.
- Excellent relationship management skills, including strong competency in vendor management and cultural awareness.
- Demonstrated mastery in developing and implementing comprehensive development plans.
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 \& Belonging
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:
Learning \& DevelopmentJob Level:
Master
"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 111,000 \- 211,000 in Colorado // 105,500 \- 243,000 in Georgia \& North Carolina \& Texas \& Virginia
– Canada: Annual Salary CAD 131,000 \- 181,000 in Ontario
The listed salary range reflects base salary. Variable incentives may also be offered.
L’échelle salariale prévue pour un nouvel embauché basé au Canada occupant ce poste est indiquée ci\-desus. L'offre peut varier dans cette échelle en fonction de l'emplacement géographique, de l'expérience professionnelle, de et/ou du niveau des compétences. S'il s'agit d'un rôle de vente, l'échelle salariale indiquée reflète le salaire de base combiné à la rémunération des ventes ciblées. S'il s'agit d'un rôle non commercial, l'échelle salariale indiquée reflète uniquement le salaire de base. Des incitations variables peuvent également être proposées. "
Information about employee benefits offered in the US can be found at https://myhperewards.com/main/new\-hire\-enrollment.html
Information about employee benefits offered in Canada can be found at https://myhperewards.com/pdf/hpe\-recruit\-brochure\-can.pdf
Des informations sur les avantages sociaux offerts sont disponibles sur https://myhperewards.com/pdf/hpe\-recruit\-brochure\-can\-fr.pdf
The estimated job application period closure is October 26 2026; this timeline is provided for transparency and internal planning purposes.
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 $111K-$243K range is above the median for AI/ML Engineer roles in our dataset (median: $175K across 2162 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 4,317 AI roles we're tracking, AI/ML Engineer positions make up 70% 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 $214,900 based on 6,420 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($177K) sits 18% below the category median. Disclosed range: $111K to $243K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Hewlett Packard Enterprise | HPE AI Hiring
Hewlett Packard Enterprise | HPE has 5 open AI roles right now. They're hiring across AI/ML Engineer. Positions span Chantilly, VA, US, Milpitas, CA, US, CA, US. Compensation range: $243K - $412K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 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 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 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 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). 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 (138) are outnumbered by mid-level (2,071) and senior (1,655) 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 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 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 $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. 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 $287,500 median, while Prompt Engineer roles sit at $145,000. 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 (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 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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