About The Role
AI is redefining what quality engineering can do. The best engineering teams have already moved on from manual test cases and long regression cycles. Tests are generated by agents. Coverage gaps are caught before code merges. Quality is enforced by the system — not by a human at the end of the pipeline.
Aspire is building that future now. We have a merge guardrail in production that reads live product specs and blocks PRs before gaps ship. We’re actively building an LLM pipeline to automatically generate tests from specs, and a live traceability matrix that keeps specs, code and test results permanently in sync. The foundation is real. The most significant capabilities are being built right now.
We’re NOT hiring a traditional QA engineer. We’re not looking for someone to execute test cases, own a regression checklist, or sit downstream of engineering waiting for a build. We’re hiring a quality engineer with the same ambition as our roadmap — someone who has already started building with AI, sees where this is going, and wants to be one of the people who gets us there.
What You’ll Own
- The AI test generation pipeline. We’re building an LLM pipeline that takes a product spec and produces automated tests — no manual authoring, no handoffs. You’ll drive it from POC to production: designing the context that ensures accurate generation, engineering prompts that hold up at scale, and validating output against live test results. When this works, it permanently changes what QA at Aspire looks like.
- The merge guardrail. It’s live. It reads the product spec at PR time and blocks the merge if coverage is missing or a critical bug is detected — before a line reaches production. Most teams haven’t built this. Your job is to make it sharper: improve its accuracy, extend it across all engineering tribes, and connect it to the generation pipeline so it creates missing tests rather than just blocking for them.
- Automation that engineers actually trust. A suite that cries wolf is worse than no suite at all. You’ll drive reliability through synthetic test data, smarter failure classification, and risk‑based test selection. Every alert should mean something.
- Quality ownership for your domains. Not execution. Ownership. You understand the spec, the code and exactly what breaks when something goes wrong. You’re in the design conversation, not the post‑release retrospective. In a regulated fintech handling real money for 50,000+ businesses, a quality gap is not a process failure — it’s a customer failure.
- Security regression in CI. Auth misconfigurations in fintech reach customers before anyone notices. You’ll build automated security regression tests across our highest‑risk API endpoints so every auth boundary is verified at merge, every time. MAS Singapore requirement. Yours to build from scratch.
What We’re Looking For
- You’ve shipped with AI — and learned from it. Not a tutorial. Not a plan. Something that ran — an automation, a tool, an LLM workflow that replaced real manual work. It doesn’t have to be large. It has to be real. You hit the failure modes, you worked around them, and you can talk about what you’d do differently. That’s the bar.
- You’re an engineer who happens to specialise in quality. You build frameworks and pipelines not just test cases. You read backend code and API contracts to find what needs testing. You trace failures to root causes in the application, not just the test.
- You hold quality to a high standard and it shows. A flaky test offends you. A coverage gap that ships to production is a failure you want to understand and prevent. You fix root causes, not symptoms.
- You move without waiting. You identify the problem, form a hypothesis from the data, and come with a proposed fix. You don’t raise a flag without a solution. You don’t wait to be assigned the work.
- You’re excited by the roadmap, not intimidated by it. You don’t need everything figured out before you start. You want to be in the room where the direction gets set — and you want to influence it.
Technical Skills
- Strong Playwright experience (TypeScript preferred) — you write clean, maintainable test architecture, not record‑and‑playback.
- Has shipped something with AI—an automation, agent, or LLM workflow that replaced real manual work. Side project, internal tool or POC all count. Talking about it doesn’t.
- Solid CI/CD integration experience (GitHub Actions, Jenkins) — you think about what to run, when and why.
- Comfortable reading backend code and API contracts to understand what needs testing.
- 3+ years in software testing, with at least one meaningful fintech or regulated‑environment project.
- Has owned quality end‑to‑end for a product domain — not just executed what was assigned.
- Familiarity with complex financial workflows — payments, KYB/KYC, transaction processing — is a strong plus.
What This Role Is NOT
- Executing test cases someone else designed.
- Sitting at the end of the delivery pipeline waiting for a build.
- Maintaining a regression suite without questioning whether it should exist.
- Marking failures as known issues and moving on.
- Treating AI as something to explore one day.
In Your First 90 Days, You’ll Probably
- Read the codebase, not just the docs. Form your own view of the three biggest quality gaps — from data, not conversations.
- Make a measurable contribution to the LLM test generation pipeline, the merge guardrail, or the automation reliability work.
- Become the person developers come to before they raise a Jira ticket.
- Challenge at least one thing we currently do — with data — and make the case for a better way.
We’re a team of ex‑founders and people who chose hard problems. If building the future of quality engineering in fintech sounds like the right challenge, we’d like to talk.