<>About the Role
This is a hands-on product architecture role supporting the Fixed Assets domain within a Tax product suite. You will own product-level architecture across core Fixed Assets workflows — asset management, depreciation, reporting, and integration to downstream tax products — while simultaneously driving the design of AI-assisted ingestion and import experiences that make it meaningfully faster and safer for clients to bring their data into the platform.
The scope of this role spans data model design, API contract definition, domain boundary governance, platform security alignment, and targeted POC work to de-risk technical unknowns before they become delivery blockers. You will serve as the architectural connective tissue between Product, UX, Engineering, and platform teams — translating SME intent into buildable, traceable specifications and keeping delivery moving at greenfield speed without compromising tax compliance or data integrity.
This is not a slides-only or advisory role. You will build spikes, produce governance artifacts, author ADRs, and maintain a clear design trace that keeps the team one sprint ahead of ambiguity.
<>What You Will Do<>Product Architecture Ownership
You will own the end-to-end product architecture for Fixed Assets — covering the domain data model, domain boundaries, API contracts, and integration patterns connecting Fixed Assets to the broader tax product ecosystem. You will architect the interaction between the UI layer, API services, automation components, and calculation systems, defining NFRs and observability requirements that make the platform predictable, measurable, and maintainable at scale. Platform alignment is also in scope: you will support API gateway pattern adoption, security pipeline design, and key management strategy, and define clear transition states so that teams can move incrementally without breaking existing integrations.
<>AI-Assisted Import & Ingestion Design
A central workstream for this role is designing AI-assisted import workflows — covering CSV and Excel ingestion, intelligent column mapping, and multi-stage validation — with explicit evaluation criteria and safety gates that make AI assistance trustworthy, not just convenient. You will drive POCs to validate the import mapping agent, explore synthetic data generation for AI training datasets, and conduct performance sizing exercises — documenting all architectural decisions and go/no-go outcomes via ADRs. The goal is an ingestion experience that is measurably accurate, has a well-defined fallback path when AI confidence is insufficient, and can be extended as the data model evolves.
<>Architecture Governance & Delivery Enablement
You will own the full suite of governance artifacts for key epics across asset management, reporting, and integration: ADRs, threat models, and measurable acceptance criteria that Engineering and QA can act on without ambiguity. You will translate ongoing SME research and product feedback into a coherent, continuously maintained architecture and design trace that keeps the team one sprint ahead — reducing rework, preventing mid-sprint surprises, and enabling stable parallel workstreams across UX, API, calculation, and integration teams. Stable contracts are a primary output: when interfaces are clearly defined and agreed, teams can move in parallel without blocking each other.
<>Outcomes & Measures
Within the first weeks of engagement, architecture artifacts — data model, security patterns, and core workflow designs — will be agreed, documented, and provide a de-risked path forward for the team. The import mapping POC will demonstrate AI-assisted ingestion with measurable accuracy and quality metrics alongside a clearly defined safe fallback path. A platform integration plan for API gateway and security pipelines will exist with acceptance criteria and rollout steps that both product and platform teams have aligned on. Stable interface contracts will be in place enabling genuine parallel work across UX, API, calculation, and integration workstreams.
<>Required Qualifications
You bring 8+ years in software engineering and at least 3+ years in product architecture for SaaS platforms, with demonstrated experience designing and owning data-heavy ingestion systems and validation workflows. You are comfortable building spikes, measuring outcomes against acceptance criteria, and using that evidence to drive architectural decisions. You have solid Azure and Kubernetes fundamentals and apply observability and NFR-driven design principles in practice. Your documentation is strong and traceable — diagrams, ADRs, NFRs, and a clear line from SME intent through design to buildable work items. You work effectively across Product, UX, and Engineering, and you are skilled at driving alignment when requirements are ambiguous and stakeholders are distributed.
<>Preferred Qualifications
Domain experience in tax-adjacent or accounting systems — particularly fixed assets, depreciation schedules, or regulatory reporting workflows — is a strong differentiator. Experience designing agentic AI in product workflows — import assistants, intelligent mapping tools, evaluators — is equally valuable, as is experience with eventing and streaming patterns for integrations and audit trail design. Familiarity with API gateway migration patterns and zero-trust service boundary design is a significant plus, given the platform alignment work that is central to this role. Experience with Microsoft Foundry Agent Service / MAF or equivalent orchestration tooling is preferred.
<>Tools & Environment
You will work primarily in VS Code with Copilot Chat/Agents. Core languages are C# / .NET for production services and Python / TypeScript for POCs and evaluation harnesses. The cloud platform is Azure — AKS/Kubernetes, Key Vault, Blob/ADLS, App Gateway/Ingress, and Monitor/Log Analytics. CI/CD runs on Azure DevOps or GitHub Actions with IaC in Bicep or Terraform. Architecture documentation lives in Markdown with Mermaid + C4 diagrams in /docs and ADRs in /docs/adr. The data ingest stack includes parsing and validation pipelines alongside dataset curation tooling for AI evaluation. AI orchestration leverages Foundry Agent Service / MAF or equivalent where appropriate.
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