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Aspire is a Series C fintech company building a financial operating system for global founders, trusted by 50,000+ startups worldwide. The company combines banking, software, and automation into a single platform, backed by Y Combinator, Peak XV, and Lightspeed.
You will design, build, and ship AI systems that automate software quality across Aspire's engineering organization. This is a high-impact role where you own multiple interconnected systems:
**Key Ownership Areas:**
1. **AI Test Generation Pipeline**: Build the LLM system that reads product specs, identifies coverage gaps and testability issues, and generates automated tests. You'll own context retrieval across specs and codebases, prompt and agent design, evaluation frameworks for generation accuracy, and feedback loops based on live test results. Take this from POC to production.
2. **Merge Guardrail**: The guardrail is already live and blocks merges when coverage is missing or critical bugs are detected. You'll improve its precision, scale it across all engineering tribes, and integrate it with the generation pipeline so it proactively creates missing tests rather than only blocking.
3. **Reliable Automation Tooling**: Build the intelligence layer behind automation, including synthetic test data generation, AI agents that explore and verify flows, automated failure classification, and risk-based test selection to ensure every alert is accurate and actionable.
4. **Quality Platform Ownership**: Understand spec, code, and failure modes, embedding that understanding into systems. Contribute at the design stage, not after release. Work closely with QA engineers to embed AI-assisted practices and use quality data to drive decisions.
5. **Security Regression in CI**: Design and build automated security regression tests for high-risk API endpoints, ensuring every auth boundary is verified at merge (MAS Singapore requirement).
In a regulated fintech serving 50,000+ businesses, quality gaps directly impact customers. You'll identify problems, validate them with data, and propose solutions independently. Your first 90 days will likely involve becoming the go-to engineer for building quality into workflows, challenging existing practices with data, identifying the three biggest quality gaps, and delivering measurable improvements.
**Requirements:**
- 5+ years in software engineering with meaningful exposure to development, testing, quality, or developer tooling, preferably including a fintech or regulated-environment project
- Proven experience shipping AI systems: built and deployed an LLM application, agent, developer tool, or automation that replaced real manual work
- Strong software engineering in TypeScript and/or Python, with experience building production services, internal tools, or developer platforms
- Hands-on experience building LLM applications, including prompt engineering, RAG or context retrieval, agents or tool use, and output evaluation (side projects, internal tools, or POCs count if shipped)
- Strong day-to-day use of AI coding tools and agentic workflows
- Solid understanding of test automation and test architecture; Playwright experience is a strong plus
- CI/CD experience (GitHub Actions, Jenkins), including building tooling that integrates at PR and merge stages
- Ability to read and reason about backend code, API contracts, and service boundaries
- Experience owning a system or product domain end-to-end
- Familiarity with financial workflows such as payments, KYB/KYC, and transaction processing is a strong plus
- Understanding of common AI failure modes such as hallucinations, context limits, and evaluation gaps, and how to address them
- High standards: address root causes rather than symptoms, treat production defects and flaky tests as system-level problems
- Comfort with ambiguity and evolving roadmaps; motivated to help shape direction
