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Solution Architect - HPC/AI (Federal)
This role has been designated as ‘Remote/Teleworker’, which means you will primarily work from home.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:
HPE is seeking a Solution Architect to support Federal High Performance Computing and Artificial Intelligence Sales. The Solution Architect (SA) is responsible for delivering business value to HPE customers by architecting effective end-to-end solutions, which address the customer’s business problems, needs, and opportunities in a manner consistent with HPE’s strategic and business goals. As the primary technical interface with the customer, candidate must be able to recognize customer needs, interpret them and produce comprehensive solutions.
Territory: US East
Responsibilities:
- Conduct product presentations, system architecture and design, developing technical responses to RFIs/RFPs, and supporting customer expectations and escalations.
- Demonstrates in-depth knowledge of high performance computing, company products and solutions as well as customers' technical and business environment. Applies knowledge to translate the functional view into a technical view and architect complex solutions and deliver joint business success.
- Partners with the account team to build the customer relationship at all levels by advocating and presenting technical strategies for customer's transformation. Reviews account pipeline to prioritize pursuit activities, and moves opportunities to close, ensuring proper utilization of time and resources.
- Solution evangelist for a specific technology or domain. Participates in deep-dive discussions and applies market intelligence and thought leadership to enhance proposed solutions and drive opportunity/demand generation for solution area(s).
- Leverages a deep knowledge of partner products by mapping the right partner to the right solution opportunity based partner on competencies and go-to-market strengths in relevant areas.
- Creates formal networks for collaboration and best practice sharing. Mentors and guides new solution architects in conceptual designs, technical depictions, and customizations essential for solution development.
Education and Experience:
- University technical degree or equivalent technical qualifications. Advanced degree in technology preferred.
- 8+ years of technical experience in IT with a focus on technical consulting and solution selling.
- Designing large scale solutions utilizing various network topologies.
- Knowledge-based as well as 1+ experienced-based relevant industry certifications very strongly preferred.
- Enterprise architecture frameworks and project management methodologies and certifications are helpful, but not required.
- Travel up to 25% based on customer and business needs.
Knowledge and Skills:
- Advanced experience participating in and leading solution configurations and the creation of demos and proofs-of-concept (POC) to meet customer requirements.
- Advanced understanding of the company portfolio of products, software, services, solution domain specialization, adjacent solution domain(s), and how they can be combined to address customer needs.
- Advanced ability to translate aaS and complete ecosystem, differentiated value, solutions, and workloads and ability to prioritize aaS offerings and consumption models that will achieve the customer’s outcomes and greatest return.
- Advanced understanding of aaS business model variations.
- Advanced ability to collaborate cross-functionally to ensure consistency of output and meet aaS strategic goals.
- Advanced written and verbal communication skills, including active listening and storytelling, and the ability to communicate in English and applicable local languages as needed to perform job requirements.
- Demonstrates advanced discussion and persuasion skills used to support company point-of-view, while respectfully questioning and challenging proposed solutions.
- Advanced business and financial acumen—sales cycle, pipeline growth, reporting, ability to influence, business strategy linkage— with an understanding of functional responsibilities of various customer business roles, including typical KPIs important to, CxOs and line-of-business (LOB) executives; knowledgeable about TCO/ROI concepts and calculations, financial models, and understand how customers generate revenue.
- Advanced consultative/value selling skills, including, whiteboarding, objection handling, and closing skills to proactively help customers make business decisions.
- Advanced company business knowledge, technical tools, and standard customer relationship management (CRM) systems and tools.
- Advanced resource management skills, including how and when to effectively engage SMEs/specialists.
- Hands on experience with one or more products, solutions, tools, or services aligned to respective job responsibility.
- Ability to design and develop a playbook for demonstrations or walk throughs of products, solutions, tools, or service.
- Ability to deliver live demonstrations or walk throughs of products, solutions, tools, or service to customers, partners, and other stakeholders.
- Advanced project and time management knowledge or experience with excellent analytical and problem-solving skills, including appropriate due diligence.
- Advanced knowledge of partner offerings and how/when to leverage them for deals within an area of specialization, understanding of the company's go-to-market strategy.
- Advanced understanding of Geo application of company’s go-to-market strategy as it relates to partners; has broad knowledge of partner offerings, along with how/when to leverage them for deals.
- Advanced strategic planning and account planning skills, as well as being an expert at using business and technical tools, and standard customer relationship management (CRM) systems.
- Knowledge of various GPUs.
- Knowledge in the design of AI Factories is a plus.
- Knowledge of HPC, Infiniband, Spectrum X preferred.
#LI-Remote
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.
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SalesJob Level:
TCP_04
"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 146,000 - 343,000 in Idaho & Maryland & New Mexico & Tennessee & 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 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 $146K-$343K range is above the 75th percentile 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 in Demand for This Role
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 ($244K) sits 15% above the category median. Disclosed range: $146K to $343K.
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
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