Senior AI QA Engineer

Requirements:

• 7+ years in QA, with a track record of owning quality across complex, modern systems — not just executing someone else's test plan.
• Deep programming ability. You are extremely strong in at least one language. We prefer TypeScript across our stack, but if your fundamentals are solid in another language, we're confident you'll pick it up fast.
• Automation expertise for microservices. You've built and scaled test automation for distributed, service-oriented architectures. You understand contract testing, API testing, and how to keep test suites fast and reliable.
• Performance testing proficiency. You have hands-on experience with performance and load testing tools — we prefer K6, but equivalent depth with other tools (Gatling, Locust, JMeter) works too. You understand how to design realistic load models and interpret results meaningfully.
• CI/CD fluency. You've integrated test automation into CI/CD pipelines and understand how to keep builds green and feedback fast. Experience with AWS infrastructure is strongly preferred.
• Monitoring and observability know-how. You're comfortable with tools like Datadog, Grafana, CloudWatch, or similar — and you use them proactively, not just when something is already on fire.
• Strong communication and presence. You participate fully in agile ceremonies, product discussions, and cross-team conversations. You don't sit quietly when something needs to be said.

As Chip evolves its product with AI-driven personalisation and intelligent automation, we increasingly value QA engineers who understand the unique challenges of testing non-deterministic systems. Experience in any of the following is a strong plus:

• Testing agentic AI systems, LLM-powered features, or recommendation engines where outputs are probabilistic rather than fixed.
• Designing validation strategies for non-deterministic behaviour — including output quality scoring, assurance testing, guardrail verification, and regression approaches that account for acceptable variance.
• Evaluating AI model outputs for accuracy, safety, bias, and hallucination.
• If you haven't worked with AI systems yet but you're curious, opinionated, and ready to learn — that matters to us too.

(Desirable) Experience in fintech, wealth management, or regulated financial services.

(Desirable) Contributions to internal quality guilds, communities of practice, or engineering culture initiatives.

(Desirable) Experience mentoring junior QA engineers or developers on testing best practices.

Beyond the skills and the experience, we want someone who operates with ownership and independence. You don't wait for permission to improve something. You see a gap in coverage, a flaky test, a missing dashboard — and you fix it. You treat the Wealth Management experience line as yours, and you hold yourself to the same standard you hold the product to. You're not a gatekeeper. You're a quality enabler. And you make every team you work with better at shipping reliable software.

What the job involves:

• We're looking for a Senior AI QA Engineer who doesn't wait to be told what to test. You'll own quality across our entire Wealth Management experience — from strategy to execution, from microservices to the UI your customers touch.
• This isn't a 'write test cases and sign off acceptance criteria' role. This is a role where you shape how quality works, where you push automation into the hands of developers, where you challenge the team to shift left, and where you hold the bar when the pressure is on.
• You'll be the quality voice in every room you walk into — product discussions, architecture reviews, incident retrospectives. When something breaks, you're already halfway through debugging it. When a new product launches, you've already stress-tested it.
• Own the Quality Strategy. You define and execute the test strategy for every new product and feature across the Wealth Management business line. You don't inherit a plan — you build one, adapt it, and make sure it holds up under real-world conditions.
• Drive Automation Where It Matters. You build and maintain automation frameworks for microservices, APIs, and end-to-end flows. Your goal is to make your team self-sufficient, not dependent on you for every regression pass.
• Lead Performance Testing. You own performance testing end to end — designing load profiles, scripting scenarios, analysing results, and working with engineering to fix what you find. You treat performance as a first-class quality concern, not an afterthought before release.
• Champion Shift-Left Practices. You embed quality thinking early in the development lifecycle. You're in design reviews asking 'how will we test this?' before a single line of code is written. You bring testing closer to development and make feedback loops shorter.
• Monitor, Observe, Debug. You have strong instincts around monitoring and observability. You know how to read logs, trace requests through distributed systems, and independently debug issues without waiting for someone to hand you a root cause.
• Operate Under Pressure. You context-switch across multiple projects without dropping the ball. You manage competing priorities, communicate tradeoffs clearly, and deliver consistently even when the pace is relentless.
• Influence and Collaborate. You work across product, engineering, and stakeholder teams. You're vocal — you raise risks early, challenge assumptions constructively, and bring people along with you rather than working in isolation.

Monday to Thursday: 9:00 AM – 6:00 PM

Friday: 8:00 AM – 4:00 PM (Start your weekend early!)