Quality Assurance Engineer

Overview Our client is an AI-powered technology company building and deploying intelligent systems that help enterprises and public sector institutions modernize operations, improve decision-making, and scale efficiently.
Operating at the intersection of applied research, advanced analytics, and software engineering, they deliver AI-driven platforms across finance, healthcare, and government — with a sharp focus on translating emerging technology into production-ready solutions that deliver measurable impact.
They are currently building a fraud detection product and expanding their AI platform portfolio.
This is an early-stage start up environment where the people they hire will shape not just what they build, but how they build it.
About the Role We are looking for a Quality Assurance Engineer to own the quality function.
This is a founding QA role - you will be setting the standards, building the frameworks, and ensuring that the AI-driven products we ship are reliable, secure, and production-ready.
We need someone who thinks deeply about quality, builds automation from the ground up, and is comfortable working closely with engineers and product teams in a fast-moving, high-stakes environment.
Responsibilities QA Strategy Ownership Define and own the QA strategy across the company's product suite, starting with the fraud detection platform.
Establish quality standards, testing frameworks, and processes that scale as the product portfolio grows Advocate for quality at every stage of the development lifecycle — from requirements through to production deployment Test Automation Design, build, and maintain robust automated test suites (functional, regression, integration, end-to-end) Build automation infrastructure from scratch, selecting the right tools and frameworks for the tech stack Ensure continuous testing is embedded in CI/CD pipelines AI API Quality Develop testing strategies for AI/ML model outputs — including accuracy validation, edge case handling, bias detection, and performance benchmarking Design and execute API testing frameworks to validate data flows, integrations, and system reliability Define data quality standards for the inputs and outputs that feed the company's AI systems Fraud Detection Product QA Lead end-to-end testing of the fraud detection product across functional, performance, security, and edge-case scenarios Design test cases that reflect real-world transaction patterns, adversarial inputs, and failure modes Work closely with the engineering and data science teams to validate model performance against defined thresholds Collaboration Process Partner with engineers, data scientists, and product managers to embed quality into the development process Document and maintain test plans, test cases, and quality reports Required Qualifications Experience Bachelor's degree in Computer Science, Software Engineering, or a related field 5+ years of experience in software quality assurance, with significant automation experience Proven track record of building QA frameworks and automation infrastructure from scratch Strong experience in API testing (REST/GraphQL) using tools such as Postman, REST-assured, or similar Proficiency in at least one automation framework (Selenium, Playwright, Cypress, PyTest, or similar) Experience with CI/CD pipelines (GitHub Actions, Jenkins, GitLab CI, or similar) Solid understanding of software development processes and the ability to work effectively with engineering teams Experience testing data-intensive or AI/ML systems is a strong advantage Experience in fintech, payments, or fraud detection systems is a strong advantage.
What We're Looking For Someone who takes genuine ownership — not waiting to be told what to test, but proactively identifying risk Comfortable working in ambiguity and building structure where none exists A systems thinker who can see how individual components interact and where things are likely to break Strong communication skills — able to articulate quality risks clearly to both technical and non-technical stakeholders An eye for edge cases, adversarial inputs, and failure modes that others miss.