Solution Architect – AI, Automation & Finance Transformation

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 selection, freshness and permission-trimmed retrieval.
• •Define the evaluation and assurance approach for AI solutions: golden datasets, accuracy and groundedness measures, regression testing on prompt or model change, and the criteria that decide whether a solution is fit to go live.
• •Design guardrails and responsible-AI controls covering prompt injection, data leakage, hallucination containment, PII handling, auditability of AI-assisted decisions, and the boundary between what the agent decides and what a person approves.
• •Set the model strategy — model selection, routing, versioning, cost and token management, and the approach to upgrades — and revisit it as the platform landscape moves.
• •Determine where machine learning, deterministic automation, or a conventional application is the right answer, and say so plainly when generative AI is not the appropriate tool for the problem.

Data and Platform Architecture

• •Design the data architecture underpinning AI and analytics use cases: source systems, ingestion patterns, curated layers, semantic models, lineage and refresh cadence.
• •Architect solutions on Microsoft Azure, Greenlake and Databricks, selecting the appropriate compute, storage, orchestration and serving components for each workload.
• •Design integration architecture across enterprise platforms such as SAP, Salesforce, Anaplan, ServiceNow and Power BI, covering API, event and batch patterns, error handling, idempotency and reconciliation between systems.
• •Define identity, access and secrets architecture using Okta, Microsoft Entra ID, OAuth, managed identities and role-based access control, and ensure least-privilege design is applied rather than assumed.
• •Set the deployment architecture across environments, including CI/CD approach, promotion path, environment parity, configuration management and rollback strategy.
• •Design for control and audit readiness where solutions touch financial data, including SOX-aligned controls, evidence capture, segregation of duties and traceability from output back to source.

Delivery Leadership

• •Lead engineers, data teams and vendor partners through implementation, reviewing designs and code against the agreed architecture and correcting drift early.
• •Build proofs of concept personally to de-risk unproven patterns, and convert what is learned into a documented pattern the team can reuse.
• •Break large initiatives into deliverable increments with clear technical dependencies, sequencing and defensible estimates.
• •Identify architectural risk, technical debt and single points of failure early, quantify the impact, and put a remediation path in front of decision-makers before it becomes an incident.
• •Support production stabilisation after release, lead root-cause analysis on significant technical failures, and feed the findings back into the architecture.
• •Raise the technical capability of the team through design mentoring, code and design review, internal enablement sessions and written guidance.

Stakeholder and Governance Engagement

• •Work directly with Finance, Operations and Transformation leadership to understand the business problem behind the request, and challenge the requirement where the stated ask will not deliver the intended outcome.
• •Present architecture, options, cost implications and risk positions to senior business and technology stakeholders, adjusting depth to the audience without diluting the substance.
• •Partner with enterprise architecture, security, infrastructure, DataOps and compliance teams to secure approvals and keep solutions aligned to enterprise standards.
• •Manage vendor and partner technical engagement, including solution assessment, scope definition, design review and acceptance of delivered work.
• •Contribute to portfolio-level planning by advising on feasibility, effort, sequencing and platform readiness across competing initiatives.

Required Skills and Experience

Architecture

• •Demonstrable experience owning solution architecture for enterprise systems that reached production and remained in service, not proof-of-concept work alone.
• •Strong command of integration architecture, API design, event-driven patterns, and data architecture across tran