Distinguished Technologist, Private Cloud AI - Applied & Agentic AI

Distinguished Technologist, Private Cloud AI

This role has been designed as 'Hybrid' with an expectation that you will work on average 2 days per week from an HPE office.

Job Description:

HPE Private Cloud solutions, delivered through HPE Green Lake, offer a way for organizations to gain the agility and pay-per-use flexibility of public cloud services within their own data centers, while maintaining control and security over their sensitive data. These solutions aim to simplify cloud management, accelerate application development, and improve resource utilization, all within a dedicated private cloud infrastructure.

We are seeking a highly skilled Distinguished Technologist – Private Cloud AI for the development 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 technical expertise and leadership across PCAI's applied AI portfolio, spanning foundation models, generative and agentic AI, intelligent automation, and AI-powered experiences for private/hybrid cloud.

• Lead AI architecture and technical design workshops with internal teams and customers to shape PCAI's applied and agentic AI strategy.
• Define customer ready AI architectures for private and hybrid cloud – spanning models, runtimes, knowledge/semantic layers, security, governance, and observability.
• Evaluate and select ISV partner technologies 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/Agent Ops, and AI security/governance blueprints, ensuring performant, reliable, and trustworthy AI systems.

• With partnership with PCAI Architecture team, set the architecture direction of Agent Ops/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 and optimize AI systems at scale and establish best practices for model lifecycle, 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 architects and provide technical leadership across engineering, applied science, and field organizations.

• Bachelor's degree in computer science or relevant field.
• At least 15+ years of progressive technical leadership and architectural experience.
• Minimum of 2 years of experience designing and implementing scaled Agentic AI and Generative AI solutions that are in production/operations.
• Minimum of 2 years of experience designing and implementing agentic AI and Generative AI platforms and frameworks that are used by multiple AI solutions, products, or teams.
• Minimum of 5 years of experience designing, engineering, and operationalizing large-scale AI/ML solutions on at least one large public cloud platform, using: cloud-native AI services and frameworks, open-source technologies, and third-party tools (e.g., observability, security, governance, data/feature platforms).
• Minimum of 5 years of expert-level understanding of key AI technologies from public cloud providers, open source ecosystems, and third-party vendors.

• Hands-on experience with foundation models and large language models (LLMs).
• Building and optimizing RAG pipelines, multi-agent systems, and tool-using agents.
• Architecting knowledge graphs and semantic layers to support AI agents and domain-specific reasoning.
• Implementing AI security, governance, and observability in production environments.
• Modern deep learning architectures (e.g., transformers, sequence…