This role sits within HPE Networking's Training and Documentation organization, which is responsible for the technical content, learning materials, and documentation that support HPE Networking's products and customers. As AI tooling becomes central to how we produce, maintain, and deliver that content, we're building out the infrastructure to support it — and this role is central to that effort. 

We're looking for an AI Infra Engineer to help us build and operate the infrastructure that lets our team actually use AI to solve real business problems specific to technical training and documentation — things like content generation assistance, documentation search and retrieval, automated content QA, Avatar-led training, and much more. This is not a research role — we're not training foundation models or running open-ended ML experiments. Instead, you'll be ideating, integrating, and productionizing existing LLMs (via vendor APIs and/or self-hosted tooling) into reliable, scalable systems that support concrete use cases across the organization. 

You'll work at the intersection of infrastructure engineering and applied AI: standing up and maintaining the pipelines, services, and tooling — whether bought from a vendor or built in-house — that make AI features work reliably in production, in service of how HPE Networking creates and maintains training and documentation content. 

What You'll Do 

• Design, build, and operate infrastructure that integrates LLMs into internal tools and customer-facing products 

• Evaluate, implement, and maintain third-party AI tooling and platforms, owning the vendor relationship and integration where applicable 

• Build homegrown tooling (pipelines, orchestration layers, retrieval systems, evaluation harnesses, monitoring) when off-the-shelf solutions don't fit 

• Own the operational reliability of AI-powered systems: uptime, latency, cost, and observability 

• Implement guardrails around cost, rate limits, prompt/version management, and failure handling for LLM-backed services 

• Collaborate with documentation and training stakeholders to translate use cases into working, maintainable systems 

• Set up monitoring, logging, and telemetry to understand how AI systems are performing in production 

• Contribute to internal standards and best practices for how the org evolves and adopts AI tooling 

• Act as an AI evangelist within the org — sharing knowledge, demonstrating new capabilities, and helping teammates understand how and where AI tooling can be applied to their work 

What We're Looking For 

• 5+ years of experience in software engineering, infrastructure, platform, or DevOps/SRE roles 

• Strong scripting/programming ability — Python, Java, etc. 

• Experience working with one or more of the following: prompt management, retrieval-augmented generation, agentic tooling, skills, MCP, etc. 

• Practical experience integrating third-party APIs into production systems (LLM APIs a strong plus) 

• Experience with cloud infrastructure (AWS, GCP, or Azure) and standard infra-as-code practices 

• Hands-on experience with containers and container tooling 

• Comfortable operating in both Linux and Windows server environments 

• Experience standing up and maintaining web services (APIs, web apps, backend services) 

• Solid understanding of building reliable, observable, and cost-conscious backend systems 

• Comfort working with both vendor-provided tools and building custom solutions when needed — you're pragmatic about buy vs. build 

• A bias toward iteration over perfection — getting usable solutions in front of users quickly, and enhancing them incrementally over time 

What This Role Is Not 

• Not a research scientist or ML research role — no model training, fine-tuning research, or novel architecture work 

• Not focused on academic experimentation — the focus is on applying existing, available AI capabilities to real use cases 

• Not a pure data science role 

Nice to Have 

• Experience with LLM observability/eval tooling (e.g., tracing, evals, prompt versioning systems) 

• Experience running self-hosted models or inference infrastructure 

• Background in automation, telemetry, or network/systems infrastructure work