AI Value Architect - Private Cloud AI

About the role

Hewlett Packard Enterprise is hiring an AI Value Architect for its LASER region, working remotely. The role owns the day-2 value, adoption and expansion lifecycle for a portfolio of strategic Private Cloud AI customers, acting as a trusted technical advisor after initial deployment. It is a customer-facing technical sales role, not a delivery or consulting role, and combines technical credibility in enterprise and generative AI with commercial judgment.

Key responsibilities

• Drive continued internal adoption, value realization and expansion of Private Cloud AI across business units, and discover new AI, GenAI and agentic AI use cases.
• Provide architecture guidance for inference, RAG, AI agents, model serving, fine-tuning, data pipelines, observability, governance and security.
• Enable technical and business champions, share roadmap updates and help teams plan their next AI initiatives.
• Run quarterly value, adoption and roadmap reviews and frequent onsite engagements, travelling frequently within LASER and occasionally worldwide.
• Identify and qualify expansion opportunities, run workshops, build the technical and commercial value case and hand validated opportunities to Sales.
• Build a global playbook of workshops, discovery guides, assessment tools, reference architectures and proof patterns, and feed field learnings back to Product and global teams.

Required profile

• Demonstrated success in a customer-facing technical sales or advisory role (e.g., AI Solutions Architect, Value Architect, Pre-Sales Engineer, Field CTO).
• Ability to translate complex AI technology into business value, run workshops and influence without direct authority.
• Program and account management across multiple strategic customers, with strong written and verbal English; Spanish and Portuguese are an advantage.
• Comfortable building a new motion from scratch, owning work in ambiguity, collaborating across GEO and global teams, and travelling frequently.

Required skills

• Enterprise AI and GenAI architectures: LLM inference, RAG, vector databases, AI agents, model endpoints, guardrails, LLMOps/MLOps.
• Production AI solutions with Python, Linux, containers, Kubernetes, Helm, APIs, identity and access controls, and object/file storage.
• Data engineering and ML platforms such as Airflow, Kubeflow, MLflow, Ray, model serving frameworks and observability tools.
• Working knowledge of GPUs, power cooling and networking; experience with NVIDIA AI Enterprise, HPE Private Cloud AI or similar platforms is strongly preferred.