Auto QA Engineer – AI Innovation

The QA Engineer will play a key role in building, testing, and maintaining quality assurance solutions that support AI‑driven applications. The role also contributes to the design of governance frameworks to ensure the organisation’s AI initiatives remain robust, reliable, and compliant.

Key Responsibilities

Collaborate closely with data scientists and AI engineers to bring AI/Generative AI models (e.g., LLMs, RAG‑based systems) into production environments.

Develop and maintain full‑stack web applications—including front‑end, back‑end, and database components—with emphasis on performance, security, and scalability.

Build and manage CI/CD pipelines (e.g., GitLab CI, Jenkins), incorporating automated tests, security scans, artefact management, and multi‑environment deployments.

Troubleshoot production incidents, perform root‑cause analyses, and drive continuous improvements in system stability.

Actively participate in Agile practices, offering accurate task estimates and providing progress updates to stakeholders.

Prepare and maintain documentation covering architecture, APIs, deployment procedures, and test frameworks to support knowledge sharing and audit readiness.

Keep up to date with emerging technologies and champion best practices within the team.

Qualifications & Experience

Bachelor’s degree in Computer Science, Information Technology, or a related discipline.

At least 4 years of hands‑on QA testing experience , with strong familiarity across key QA processes and tools such as Cypress, Selenium, Playwright.

Proficiency in JavaScript/TypeScript is essential

Experience in AI governance or evaluation frameworks is highly preferred (e.g., RAGAS, Promptfoo, LangSmith, AI guardrail assessment).

Demonstrated experience building CI/CD pipelines using GitLab CI or Jenkins, including automated testing, container builds, and environment promotion.

Solid understanding of cloud technologies (AWS, Azure, or Google Cloud) and orchestration platforms like Kubernetes.

Exposure to AI/ML technologies (TensorFlow, PyTorch, LangChain, etc.) and implementing them within microservices architectures is advantageous.

Financial services knowledge is a plus.

Strong communication abilities and a data‑driven approach to problem‑solving.

Full-time