Distinguished Technologist, Private Cloud AI

Distinguished Technologist, Private Cloud AI
- Applied & Agentic AIThis 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 onfinding 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 Private Cloud solutions, delivered through HPEGreenLake, offer a way for organizations to gain the agility and pay-per-use flexibility of public cloud services within their own data centers, whilemaintainingcontrol and security over their sensitive data. These solutions aim to simplify cloud management, accelerate application development, and improve resourceutilization, all within a dedicated private cloud infrastructure.
We areseekinga highly skilledDistinguished Technologist–Private Cloud AIdevelopment and advancement of the AI Software products.
As a Distinguished Technologist within the Private Cloud AI group, you are a hands‑on senior architect and AI leader who defines and delivers the future of AI for our private cloud offerings – one big idea at a time.
You will provide deep technicalexpertiseand leadership across PCAI’s applied AI portfolio, spanning foundation models, generative and agentic AI, intelligent automation, and AI‑powered experiences for private/hybrid cloud.
Strategy, Architecture & DesignLead AI architecture and technical design workshops with internal teamsand customersto shapePCAI’s applied and agentic AI strategy.
Definecustomer readyAI architecturesfor private and hybrid cloud – spanning models, runtimes, knowledge/semantic layers, security, governance, and observability.
Evaluate and selectISVpartnertechnologies for the AI platform stack,develop TCO and roadmap options, and drive key architectural decisions for PCAI products and customer solutions.
Design multi‑agent,LLMOps/AgentOps, and AI security/governance blueprints, ensuringperformant, reliable, and trustworthy AI systems.
Implementation & Technical LeadershipWith partnership withPCAIArchitecture team, settheArchitecturedirectionofAgentOps/LLMOps, AI Governance, AI Security, and AI Observability capabilities in PCAI offerings.
Build reusable AI components and agents and partner with engineering to take POCs into scalable, production‑grade services.
Troubleshoot andoptimizeAI systems at scale andestablishbest practices for modellifecycle, evaluation, and responsible AI.
Recommend design optimizations and improvements for performance, cost efficiency, reliability, and trustworthiness.
Create and present high‑impact technical content(reference architectures, design patterns, whitepapers, conference talks, and internal/external publications) to influence customers, partners, and internal stakeholders.
Mentor senior engineers and architectsand provide technical leadership across engineering, applied science, and field organizations.
Experience and SkillsBachelor's degree in computer science or relevant fieldAt least 15+ years of progressive technical leadership andarchitectural experience.
Minimum of2 yearsof experiencedesigning and implementing scaled Agentic AI and Generative AI solutionsthat are in production/operations.
Minimum of2 yearsof experiencedesigning and implementing agentic AI and Generative AI platforms and frameworksthat are used by multiple AI solutions, products, or teams.
Minimum of5 yearsof experiencedesigning, engineering, andoperationalizinglarge‑scale AI/ML solutionson at least one large public cloud platform, using:Cloud‑native AI services and frameworks,open‑source technologies, andthird‑party tools (e.g., observability, security, governance, data/feature platforms).
Minimum of5 yearsofexpert‑level understanding of key AI technologiesfrom public cloud providers,open sourceecosystems, and third‑party vendors.
Technical Depth (AI/ML and GenAI)Hands‑on experience with:Foundation models and large language models (LLMs).
Building andoptimizingRAG pipelines, multi‑agent systems, and tool‑using agents.
Architectingknowledge graphs and semantic layersto support AI agents and domain‑specific reasoning.
ImplementingAI security, governance, and observabilityin production environments.
Modern deep learning architectures (e.g., transformers, sequence models),.
Pre‑training, fine‑tuning, distillation, and alignment of models.
Cloud, Platform, and Architecture SkillsStrong understanding ofcloud‑native architectures(containers, microservices, Kubernet