Senior QA Engineer
We’re looking for a technically strong Senior QA Engineer to help build quality into our software development lifecycle.
This is a hands-on Quality Engineering role—not just manual testing. You’ll design and maintain automated testing frameworks, improve QA processes, integrate testing into CI/CD, and work closely with Engineering and Product from requirements through release.
You’ll also play an important role in our use of AI and LLM tools, helping the team use AI effectively while maintaining quality, security, privacy, and engineering standards.
What You’ll Do
Design, build and maintain automated testing frameworks and regression suites.
Develop automated tests across frontend, backend, APIs, databases, integrations and end-to-end workflows.
Perform functional, exploratory, regression and manual testing where appropriate.
Integrate automated testing and quality gates into CI/CD pipelines.
Work with developers to investigate failures, identify root causes and improve test reliability.
Review requirements and acceptance criteria early to identify quality, security, integration and performance risks.
Support defect management, release testing and UAT.
Test authentication, authorisation, APIs, data security and common OWASP risks.
Perform performance, load and scalability testing.
Validate browser, device, accessibility and usability requirements.
Help establish QA standards, processes and best practices across Engineering.
AI & LLM Quality Engineering
AI is an important part of how we build and test software. You’ll be expected to:
Use AI coding assistants and LLM tools for test design, automation, debugging, code review, documentation and defect analysis.
Evaluate AI-generated code, tests and analysis for correctness, security, maintainability and coverage.
Build practical AI-assisted QA workflows that improve delivery without replacing engineering judgement.
Use AI tools responsibly and protect source code, credentials, customer information and confidential data.
Where our products use AI/LLMs, test areas such as prompt handling, hallucinations, grounding, tool use, guardrails, privacy, security, latency and reliability.
Help create repeatable LLM evaluation and regression testing approaches.
Technology
Strong hands-on experience with the following is expected:
Node.js, NestJS and Next.js
JavaScript / TypeScript
PostgreSQL and relational databases
REST APIs
Authentication and authorization systems
Modern frontend and backend architectures
Experience with tools such as Playwright, Cypress, Jest, Vitest, Supertest, Postman, Selenium, k6 or JMeter is valuable. We care more about your ability to choose the right testing approach than expertise in one particular tool.
Experience with AI application technologies such as model APIs, RAG, embeddings, vector search, structured outputs, tool calling or AI agents is also valuable.
What We’re Looking For
Mid–Senior level experience in QA, Quality Engineering or Test Automation.
Strong experience building and maintaining automated test frameworks.
Strong JavaScript/TypeScript automation skills.
Hands-on experience testing Node.js, NestJS, Next.js and PostgreSQL applications.
Strong API, integration and full-stack web application testing experience.
Experience integrating automated testing into CI/CD pipelines.
Good understanding of frontend, backend, database and authentication testing.
Experience with security, performance and load testing.
Strong analytical and problem-solving skills.
Ability to investigate issues using logs, browser tools, API responses and database queries.
Strong communication and collaboration skills.
Practical experience using AI/LLM tools in software development or QA.
Ability to critically evaluate AI-generated code, tests and analysis.
Understanding of responsible AI use, including data privacy, security and confidential information.
Experience testing AI/LLM-powered features is highly desirable.
Nice to Have
Agile/Scrum experience.
Experience establishing QA processes or frameworks.
Microservices or service-based architecture experience.
Cloud-hosted or containerized application experience.
Third-party integration testing.
Accessibility testing experience.
Experience with LLM evaluation, evaluation datasets or AI quality monitoring.
What Success Looks Like
You’ll help us move from “testing at the end” to quality throughout development.
Success means reliable automation protects critical workflows, developers receive fast feedback, risks are identified early, releases have clear testing evidence, and AI helps us move faster without compromising quality, security or engineering judgement.