Solution Architect – AI, Automation & Finance Transformation

Hewlett Packard Enterprise • Spring, TX

15h • Onsite

About The Position

Hewlett Packard Enterprise is seeking a Solution Architect to own the end-to-end architecture of enterprise AI, automation and analytics solutions across its Finance Transformation portfolio. This is a hands-on design-and-decide role: the architect sets target-state architecture, selects platforms and patterns, obtains enterprise technology, security and data-governance approvals, and remains involved through delivery to ensure the implemented solution matches the design.

The role translates Finance and Operations needs—including forecasting, reporting, reconciliation, transfer pricing, deal support and backlog analytics—into solutions that are scalable, secure, governable and supportable. The architect owns architecture standards and non-functional design, establishes reusable patterns, and guides engineering teams and vendor partners through implementation. The position includes personally writing code and building proofs of concept to validate new patterns, as well as contributing to production stabilisation and root-cause analysis.

The architect designs AI, GenAI, agentic AI, automation and analytics solutions; their data, integration, identity, deployment and control architectures; and the evaluation and responsible-AI measures needed for production readiness. They work across platforms including Microsoft Azure, GreenLake and Databricks, and enterprise systems such as SAP, Salesforce, Anaplan, ServiceNow and Power BI. They engage Finance, Operations, Transformation leadership and enterprise technology partners on solution options, feasibility, cost, risk and governance.

The position requires at least 10 years of experience in software engineering, data or AI, including at least three years in solution architecture or technical leadership. Candidates should have delivered enterprise systems into production, recent hands-on experience with enterprise GenAI or agentic AI solutions, and a bachelor's or master's degree in a relevant discipline or equivalent industry experience. The role is onsite, primarily from an HPE office. The expected U.S. base salary range is USD 157,000–361,000 annually in Texas; variable incentives may also be offered.

Requirements

• Demonstrable ownership of solution architecture for enterprise systems that reached production and remained in service; proof-of-concept work alone is insufficient.
• Strong knowledge of integration architecture, API design, event-driven patterns and data architecture across transactional and analytical estates.
• Ability to produce architecture documentation and decision records that withstand enterprise architecture and security review.
• Experience designing for scale, resilience, performance, observability and cost, with evidence that designs performed in production.
• Hands-on experience architecting and delivering enterprise GenAI or LLM solutions, including RAG, tool and function calling, and agentic orchestration.
• Working knowledge of agentic frameworks or platforms such as LangChain, LangGraph, Semantic Kernel, AutoGen or Copilot Studio, or equivalents.
• Experience with Azure OpenAI or comparable enterprise LLM platforms, including evaluation, guardrails and cost management at scale.
• Understanding of the machine-learning lifecycle, including feature engineering, training, deployment, monitoring and drift, sufficient to architect ML solutions and govern delivery teams.
• Experience with enterprise automation and RPA platforms, and sound judgment about when automation is preferable to an AI-first approach.
• Current, hands-on Python skills sufficient to prototype, review code and debug production issues.
• Strong SQL skills and practical experience with Databricks, cloud data warehouses and modern data pipelines.
• Solid grounding in Microsoft Azure services, containerisation, CI/CD and infrastructure-as-code practices.
• Working knowledge of Power BI, Power Platform and the Microsoft 365 ecosystem as delivery surfaces for Finance solutions.
• Experience delivering enterprise Finance solutions in areas such as forecasting, financial reporting, reconciliation, transfer pricing, deal desk, tax, or order and backlog analytics.
• Understanding of financial controls, audit expectations and governance constraints for systems handling financial data.
• At least 10 years of experience in software engineering, data or AI, including at least three years in a solution architecture or technical lead capacity.
• Track record of architecting and delivering multiple enterprise solutions into production and through post-release support.
• Recent hands-on delivery of GenAI or agentic AI solutions in an enterprise setting.
• Bachelor's or master's degree in Computer Science, Information Technology, Engineering, Data Science or a related discipline; equivalent industry experience will be considered.

Nice To Haves

• Recognised architecture certification, such as Azure Solutions Architect Expert, Databricks or TOGAF.
• Experience establishing AI governance, model risk or responsible-AI frameworks in a regulated enterprise.
• Experience defining reusable platform capabilities or an internal developer experience adopted across multiple teams.
• Exposure to FinOps and cloud cost optimisation for AI and data workloads.
• Experience operating in a global organisation across multiple regions and time zones.

Responsibilities

• Own the target-state architecture and roadmap for AI, GenAI, agentic AI and automation solutions across the Finance Transformation portfolio.
• Produce enterprise-standard architecture artefacts, including context and component diagrams, integration and data-flow designs, sequence flows, non-functional requirements and architecture decision records.
• Make and explain build-versus-buy, platform-selection and architecture-pattern decisions, including trade-offs in cost, delivery time, supportability and risk.
• Define reusable reference architectures, solution patterns and shared components; maintain a current view of the solution landscape and prevent duplicate or divergent builds.
• Own non-functional design for performance, scalability, availability, cost, observability and supportability, and set production acceptance thresholds.
• Run design reviews and technical governance forums, and take solutions through enterprise architecture, security and data-governance reviews.
• Architect LLM and agentic solutions, including orchestration, tool and function calling, retrieval and grounding, memory and state, human approval checkpoints and fallback behaviour.
• Design retrieval-augmented generation over enterprise content, including chunking, embeddings, indexes, source-of-truth selection, freshness and permission-trimmed retrieval.
• Define AI evaluation and assurance approaches, including golden datasets, accuracy and groundedness measures, regression testing and go-live criteria.
• Design responsible-AI guardrails for prompt injection, data leakage, hallucinations, PII, auditability and human approval boundaries.
• Set model strategy for selection, routing, versioning, cost and token management, and upgrades; determine when ML, deterministic automation or conventional applications are more appropriate than generative AI.
• Design data architecture for AI and analytics, including source systems, ingestion, curated layers, semantic models, lineage and refresh cadence.
• Architect solutions on Microsoft Azure, GreenLake and Databricks, selecting appropriate compute, storage, orchestration and serving components.
• Design API, event and batch integrations across platforms such as SAP, Salesforce, Anaplan, ServiceNow and Power BI, including error handling, idempotency and reconciliation.
• Define identity, access and secrets architecture using technologies such as Okta, Microsoft Entra ID, OAuth, managed identities and role-based access control, applying least privilege.
• Set deployment architecture across environments, including CI/CD, promotion paths, environment parity, configuration management and rollback.
• Design for financial control and audit readiness, including SOX-aligned controls, evidence capture, segregation of duties and traceability to source data.
• Lead engineers, data teams and vendor partners through implementation; review designs and code against the agreed architecture and correct drift early.
• Build proofs of concept personally to de-risk patterns and turn findings into documented, reusable guidance.
• Break initiatives into deliverable increments with clear dependencies, sequencing and defensible estimates.
• Identify architectural risk, technical debt and single points of failure, quantify their impact and propose remediation.
• Support production stabilisation, lead root-cause analysis of significant technical failures and incorporate findings into architecture.
• Develop team capability through mentoring, code and design reviews, enablement sessions and written guidance.
• Work with Finance, Operations and Transformation leaders to understand business problems and challenge requirements when appropriate.
• Present architecture options, cost implications and risk positions to business and technology stakeholders.
• Partner with enterprise architecture, security, infrastructure, DataOps and compliance teams to obtain approvals and align with enterprise standards.
• Manage vendor and partner technical engagement, including solution assessment, scope definition, design review and acceptance of delivered work.
• Advise portfolio planning on feasibility, effort, sequencing and platform readiness across initiatives.

Benefits

• Comprehensive benefits supporting physical, financial and emotional wellbeing for employees and their loved ones.
• Career and professional development programs intended to help employees pursue career goals.
• Variable incentives may be offered in addition to the listed base salary.

What This Job Offers

• Job Type: Full-time
• Career Level: Senior