AI Value Architect - Private Cloud AI

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. #J-18808-Ljbffr Hewlett Packard Enterprise Company