Solution Architect – AI, Automation & Finance Transformation

Overview

Own the end-to-end architecture of enterprise AI, automation and analytics solutions across the Finance Transformation portfolio, acting as a design-and-decide role that stays close to delivery.

What you'll do

• 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 architecture decision records with options considered and rationale.
• Make and defend build-versus-buy, platform-selection and pattern decisions, stating the trade-offs in cost, delivery time, supportability and risk.
• Define reusable reference architectures, solution patterns and shared components.
• Own non-functional design across performance, scalability, availability, cost, observability and supportability, and set acceptance thresholds before production.
• Run design reviews and technical governance forums, and take solutions through enterprise architecture, security and data governance approval.
• Maintain a current view of the solution landscape and prevent duplicate or divergent builds across teams.
• Architect LLM and agentic solutions end to end, including orchestration and agent topology, tool and function calling, retrieval and grounding, memory and state, human-in-the-loop checkpoints, and fallback behaviour.
• 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, including golden datasets, accuracy and groundedness measures, regression testing on prompt or model change, and go-live criteria.
• Design guardrails and responsible-AI controls covering prompt injection, data leakage, hallucination containment, PII handling, auditability, and the boundary between agent decisions and human approval.
• Set the model strategy, including model selection, routing, versioning, cost and token management, and upgrade approach.
• Determine where machine learning, deterministic automation or a conventional application is the right answer, and say plainly when generative AI is not appropriate.
• Design the data architecture underpinning AI and analytics use cases, including source systems, ingestion patterns, curated layers, semantic models, lineage and refresh cadence.
• Architect solutions on Microsoft Azure, Greenlake and Databricks, selecting compute, storage, orchestration and serving components for each workload.
• Design integration architecture across SAP, Salesforce, Anaplan, ServiceNow and Power BI, covering API, event and batch patterns, error handling, idempotency and reconciliation.
• Define identity, access and secrets architecture using Okta, Microsoft Entra ID, OAuth, managed identities and role-based access control, applying least-privilege design.
• 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 to source.
• 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 learnings into documented reusable patterns.
• 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 impact, and put a remediation path before decision-makers.
• Support production stabilisation after release, lead root-cause analysis on significant technical failures, and feed findings back into the architecture.
• Raise team technical capability through design mentoring, code and design review, enablement sessions and written guidance.
• Work directly with Finance, Operations and Transformation leadership to understand the business problem, and challenge requirements where the stated ask will not deliver the intended outcome.
• Present architecture, options, cost implications and risk positions to senior business and technology stakeholders.
• Partner with enterprise architecture, security, infrastructure, DataOps and compliance teams to secure approvals and maintain alignment with 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.

What you'll need

• Ten or more years in software engineering, data or AI, including at least three years in a solution architecture or technical lead capacity.
• Demonstrable experience owning solution architecture for enterprise systems that reached production and remained in service.
• 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 the design held in production.
• 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.
• Strong hands-on Python, retained and current, able to prototype, review code 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.
• Experience delivering solutions in enterprise Finance, such as 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.
• Bachelor's or Master's degree in Computer Science, Information Technology, Engineering, Data Science or a related discipline, or equivalent industry experience.

Nice to have

• 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.

Details

• Onsite role, with an expectation of working primarily from an HPE office.
• Location: Bangalore, Karnataka, India.
• Job level: TCP_05.