Senior Software Architect - AWS

Budgetly

2d•Remote

About The Position

Budgetly is building an AI-first platform to simplify expense management and help businesses make better financial decisions. This role is part of a small, fully remote team, reporting to the Team Lead. The company is focusing on AI-enabled workflows, and this role is crucial for the platform's infrastructure security and development. The Senior Software Architect will own architecture and technical direction across AWS, security, platform reliability, data, and AI infrastructure. This involves building and refining the platform, contributing to feature development, and dedicating 60-70% of their time to architecture-related work.

Requirements

Deep technical ownership: Led architecture decisions for a platform or system end-to-end.
Genuine hands-on orientation: Willingness to be in the codebase and build designed solutions.
Strong AWS infrastructure experience in production: Serverless, Lambda, DynamoDB, ECS Fargate, EventBridge, CDK.
Strong security expertise in backend/cloud systems: secure architecture, IAM, cloud security.
Deep backend engineering designing and evolving microservices or distributed systems.
A track record of architectural ownership, not just feature implementation.
Familiarity with agent tooling concepts: tool calling, MCP/tool integration, guardrails, evals, tracing/observability, and permissioning.
Experience with TypeScript, React, and GitHub Actions.
Full Australian Work Rights.
Located in Australia.

Nice To Haves

AWS certifications, particularly AWS Solutions Architect (Associate or Professional).
Previous experience in FinTech, payments, or finance industry.

Responsibilities

Owns architecture decisions across DevSecOps, Platform/Reliability, Data, and AI infrastructure.
Build secure-by-design practices into the platform: IAM, cloud security, secure engineering.
Set standards for platform scaling, resilience, and reliability.
Partner with the Data Engineer to build out data lake and analytics capability.
Develop AI infrastructure including model and agent integration, secure AI data pipelines, and platform-level AI guardrails.
Contribute to hands-on feature development.
Refactor code and pay down technical debt when possible.