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Solution Architect – AI, Automation & Finance Transformation

Job in Spring, Harris County, Texas, 77391, USA
Listing for: Hewlett Packard Enterprise
Full Time position
Listed on 2026-10-28
Job specializations:
  • IT/Tech
    AI Engineer (Applied/Software)
Salary/Wage Range or Industry Benchmark: 157000 - 361000 USD Yearly USD 157000.00 361000.00 YEAR
Job Description & How to Apply Below
Solution Architect – AI, Automation & Finance Transformation

This role has been designed as ‘’Onsite’ with an expectation that you will primarily work 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

We are looking for a Solution Architect to own the end-to-end architecture of enterprise AI, automation and analytics solutions across the Finance Transformation portfolio. This is a design-and-decide role with real accountability: the architect sets the target-state architecture, chooses the platforms and patterns, secures approvals from enterprise technology, security and data governance, and then stays close enough to delivery to guarantee that what ships matches what was designed.

The role sits between business intent and technical execution. It translates finance and operations problems — forecasting, reporting, reconciliation, transfer pricing, deal support, backlog analytics — into solution designs that are scalable, secure, governable and supportable, and it holds the line on architecture standards when delivery pressure argues otherwise. It is an advanced role, but not a detached one. The architect is expected to write code when a pattern needs proving, run the proof of concept personally, and lead engineers and vendor teams through the build rather than handing over a deck and stepping away.

Architecture

Ownership

Own the target-state architecture for AI, GenAI, agentic AI and automation solutions across the Finance Transformation portfolio, and maintain the roadmap that moves the estate towards it. Produce solution architecture artefacts to enterprise standard: context and component diagrams, integration and data-flow designs, sequence flows, non‑functional requirements, and documented architecture decision records with the options considered and the rationale for the choice.

Make and defend build-versus-buy, platform‑selection and pattern decisions, stating explicitly the trade‑offs in cost, delivery time, supportability and risk. Define reusable reference architectures, solution patterns and shared components so that each new use case starts from an established baseline rather than a blank page. Own non‑functional design across performance, scalability, availability, cost, observability and supportability, and set the acceptance thresholds each solution must meet before production.

Run design reviews and technical governance forums, and take solutions through enterprise architecture review, security review and data governance approval. Maintain a current view of the solution landscape — what exists, what it depends on, what is being retired — and prevent duplicate or divergent builds across teams.

AI and Agentic Solution Design Architect

LLM and agentic solutions end to end: orchestration and agent topology, tool and function calling, retrieval and grounding strategy, memory and state, human-in-the-loop checkpoints, and fallback behaviour when the model is wrong. Design retrieval‑augmented generation over enterprise content, including chunking and embedding strategy, index design, source‑of‑truth…

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