Senior QA Engineer

JuiceX•FL, FL

About The Position

We are hiring a Senior QA Engineer to own quality across the full delivery lifecycle, from business requirements through production sign-off. At a regulated exchange, trading, clearing, and the payments that fund them must be correct; quality protects customers’ money and the organization’s regulatory standing. The role works across distributed backend services in Rust, web and mobile front ends in React and TypeScript, a cloud-native platform on Kubernetes, and integrations with payment, identity, and market infrastructure partners. The Senior QA Engineer defines risk-based testing, builds and maintains automation, supports release decisions with auditable evidence, and uses AI tools and workflows to improve testing without lowering the quality bar.

Requirements

At least 5 years in QA or quality engineering, including owning release sign-off for production systems.
Strong test design and risk analysis skills.
Hands-on test automation experience; Playwright with TypeScript is preferred, along with API testing.
Comfort reading and writing code in at least one backend language; Rust is a plus.
Working SQL skills for data validation.
Regular, real-world use of AI coding and testing tools, with examples to share.
CI/CD experience.
Clear written communication.

Nice To Haves

Financial services experience, including trading, payments, or fintech.
Experience in regulated environments.
Performance and load testing experience.
Experience building AI agents, tooling, or LLM evaluations.
Mobile testing experience.

Responsibilities

Turn business requirements into clear, testable acceptance criteria before development starts.
Define a risk-based test strategy for each initiative, balancing unit, integration, end-to-end, performance, and exploratory testing.
Design, build, and maintain automated test suites and frameworks, and integrate them into CI/CD.
Review changes for test quality and coverage, and drive flaky or failing tests to root cause.
Own the go/no-go decision for production releases, with clear, auditable evidence of what was tested.
Track escaped defects, feed lessons back into requirements and tests, and raise the quality bar across the team.
Use AI to turn requirements into test scenarios, edge cases, and automation.
Run agent-driven exploratory and UI testing.
Build AI-assisted tooling for test maintenance and failure triage, and reusable QA workflows the team can apply.
Test and evaluate AI-powered product features, verifying AI output against real system behavior before sign-off.