Quality Assurance Engineer
Job Description:
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.
- Someone who takes genuine ownership — not waiting to be told what to test, but proactively identifying risk