Role:: Product Architect — Financial Systems & AI-Driven Workflows
Location:) Remote
Duration: 12 months contract to hire
Only w2
Must have: Architecture Design patterns, Architectural documentation and communication, Data Governance, AI-Assisted Workflow Design, Kafka & Financial Systems Architecture — Domain Depth. Azure & Domain experience in payments, banking, trading systems, billing, licensing, entitlements, or financial platforms. Experience integrating ERP or financial systems (SAP or equivalent). C# / .NET (primary); Python / TypeScript and Azure DevOps or GitHub. Strong communication skills…
About the Role
This is a hands-on Product Architecture role focused on designing and governing data-intensive financial systems across the product portfolio. You will define domain models, API and event contracts, and scalable workflows that support high-volume transactions, complex financial processes, and strict compliance requirements — while embedding AI capabilities safely into data quality and workflow automation.
This is a domain ownership role with real accountability. Your designs and contracts will be adopted across teams. Success is measured by your ability to handle large-scale financial data, ensure accuracy and auditability, enable reliable downstream adoption without breaking changes, and govern AI-assisted workflows with the rigor that financial systems demand.
What You Will Do
Product Architecture Ownership
- Own product-level architecture for financial systems — domain models, system flows, and integration patterns supporting large-scale data and transaction processing
- Define NFRs across latency, throughput, correctness, availability, operability, and security — with measurable acceptance criteria and live observability dashboards
- Design API-first and event-driven architectures enabling scalable, reliable consumption across downstream systems
- Establish data governance, auditability, and traceability standards across all financial workflows
- Coach delivery teams on contract design, observability, and data integrity best practices
Financial Systems & Workflow Design
- Architect solutions handling high-volume financial data, complex calculations, and multi-step workflows
- Design systems supporting compliance-heavy processes — audit trails, regulatory reporting, and reconciliation
- Define where business logic, enrichment, and validation reside — pipeline versus service layer — with idempotent processing, replay ability, and recoverability for financial transactions
- Partner with integration teams to design scalable, loosely coupled systems using event-driven patterns
AI-Assisted Data Quality & Workflows
- Apply AI capabilities safely for data mapping, normalisation, and anomaly detection — scoped as decision-support with human approval, never as autonomous mutation of financial data
- Design 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
- Design evaluation and safety gates for all AI-assisted flows to ensure outputs are auditable, correctable, and compliant with financial governance standards
- Build rapid spikes and POCs to de-risk agent workflows, retrieval and evidence patterns, and performance assumptions; document all architectural decisions via ADRs
Data Integrity, Governance & Compliance
- Design for accuracy, consistency, and auditability of financial data across systems
- Implement governance frameworks ensuring compliance with financial regulations and internal controls
- Define reconciliation strategies, exception handling, and correction workflows
- Establish monitoring and alerting for data quality SLIs
POC Execution & Technical Leadership
- Build POCs and technical spikes to validate architecture decisions around data processing, workflows, and integrations
- Translate ambiguous financial and business requirements into scalable, well-documented technical designs
- Document all architectural decisions via ADRs and maintain traceability across systems
Outcomes & Measures
- Scalable architecture supporting high-volume financial data and complex workflows delivered and adopted across teams
- Measurable improvements in data accuracy, processing reliability, and system performance
- Domain models and contracts adopted by multiple downstream systems without breaking changes
- AI-assisted workflows operational with measurable accuracy, human-approved safety gates, and audit-ready outputs
- Robust audit, reconciliation, and recovery mechanisms in place with live observability dashboards
Required Qualifications
- 8+ years in software engineering with 3+ years in product or application architecture
- Strong experience in fintech or financial systems involving large-scale data processing and complex workflows
- Hands-on experience designing systems with high data volumes, transactional integrity, and compliance requirements
- Proven ability to design data models, APIs, and event-driven systems for cross-team adoption with backward compatibility
- Experience with governance, auditability, and regulatory considerations in system design
- Strong understanding of NFRs — correctness, reliability, scalability, and observability — with measurable acceptance criteria
- Ability to translate ambiguity into clear, buildable, well-documented architecture
Preferred Qualifications
- Domain experience in payments, banking, trading systems, billing, licensing, entitlements, or financial platforms
- Kafka / Confluent — schema governance, consumer patterns, replay strategies, and event streaming at scale
- Experience integrating ERP or financial systems (SAP or equivalent) into downstream SaaS provisioning or reporting flows
- Experience applying AI/ML for anomaly detection or data quality in controlled, auditable, human-approved loops
- Cloud experience (Azure preferred) — AKS, storage, networking, monitoring — with distributed system design
- Familiarity with Microsoft Foundry Agent Service / MAF or equivalent AI orchestration tooling.
Core Competencies
- Systems thinking — design scalable, resilient architectures for complex financial workflows under real constraints
- Data integrity focus — correctness, auditability, and compliance-first mindset at every design decision
- AI pragmatism — evaluation loops, safety gates, and human approval as non-negotiable design requirements; not the assumption that the model gets it right
- Strong communication — equally effective with technical engineering teams and non-technical business stakeholders
- Pragmatic architecture — balance scalability, governance, and delivery speed without sacrificing financial data accuracy
- Design Patterns
Tools & Environment
- Languages: C# / .NET (primary); Python / TypeScript (POCs and evaluation harnesses)
- Cloud: Azure — AKS, Key Vault, Blob/ADLS, App Gateway, Monitor/Log Analytics
- CI/CD: Azure DevOps or GitHub Actions; Bicep / Terraform
- Eventing: Kafka / Confluent — schema governance and contract testing
- Diagrams & Docs: Mermaid + C4; ADRs in /docs/adr
- Data Stores: Relational and distributed systems supporting large-scale financial data
- AI Orchestration: Foundry Agent Service / MAF or equivalent where appropriate
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