Solution Architect – AI, Automation & Finance Transformation

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 transactional and analytical estates.
• Proven ability to produce architecture documentation and decision records that withstand enterprise architecture and security review.
• Experience designing for non-functional requirements — scale, resilience, performance, observability and cost — with evidence that the design held in production.

AI and Automation

• Hands-on experience architecting and delivering GenAI or LLM-based enterprise solutions, including RAG, tool and function calling, and agentic orchestration.
• Working knowledge of agentic frameworks and platforms such as LangChain, LangGraph, Semantic Kernel, AutoGen, Copilot Studio or equivalent, with a clear view of where each fits.
• Experience with Azure OpenAI or comparable enterprise LLM platforms, including evaluation, guardrails and cost management at scale.
• Understanding of the machine learning lifecycle — feature engineering, training, deployment, monitoring and drift — sufficient to architect ML solutions and govern the teams building them.
• Experience with enterprise automation and RPA platforms, and sound judgement on where automation beats an AI-first approach.

Engineering

• Strong hands-on Python, retained and current — able to prototype, review code credibly and debug a production issue.
• Strong SQL 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.

Domain

• Experience delivering solutions in enterprise Finance — forecasting, financial reporting, reconciliation, transfer pricing, deal desk, tax or order and backlog analytics.
• Understanding of financial controls, audit expectations and the governance constraints that apply to systems handling financial data.

Preferred Skills

• 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.

Candidate Profile

• Thinks in systems and trade-offs, and can hold both the enterprise view and the implementation detail at once.
• Makes decisions under incomplete information, records the reasoning, and revises the position when evidence changes.
• Retains genuine hands-on credibility and earns the respect of engineers by doing the work, not by title.
• Communicates with equal effectiveness to a CFO stakeholder and to a data engineer.
• Comfortable with ambiguity and evolving requirements, and able to shape a defensible scope from an open-ended problem.
• Willing to say no, or not yet, with reasoning and an alternative attached.
• Takes ownership beyond the design phase and treats production outcomes as part of the architect's accountability.

Experience and Education

• Ten or more years in software engineering, data or AI, including at least three years in a solution architecture or technical lead capacity.
• A 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.

Accessibility

HPE is committed to creating an inclusive and accessible workplace and encourages applications from all qualified individuals, including those with disabilities. If you believe you require accommodation during any stage of the application or interview process, please submit your request by completing our secure form linked here.

Note: This option is reserved for applicants needing assistance/reasonable accommodation related to a disability.

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