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Senior QA Automation Engineer

Onejar — Jordan · Posted ~13 hours ago

Senior Full-time

Skills

QA automation automated testing AI testing performance testing stress testing test documentation test automation AI/ML data pipelines

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Summary ✨ AI‑Generated

Join a senior QA automation team testing AI-driven applications in a demanding real-time financial environment. You will build automated test suites, validate trading logic and data pipelines, perform performance and stress testing, verify machine learning model inputs and outputs, and collaborate closely with engineering and product teams.

Highlights

Work on advanced quality engineering for AI-driven applications in a financial technology environment. The role covers automated testing, real-time trading logic, model performance, data validation, and close collaboration with AI/ML specialists.

Description

We are hiring a talented Senior QA Automation Engineer to join our client's team — a well-known fintech company. Responsibilities: Analyze and clarify requirements with engineers or project managersDevelop and maintain automated test suites for AI-driven applicationsDesign tests to validate real-time trading logicAutomate performance and stress testing for model inference under various market scenariosValidate quality and correctness of input data pipelines (market data, sentiment signals, behavioral metrics)Collaborate with ML engineers to verify input/output integrity of machine learning modelsWork closely with AI/ML developers, data scientists, and product managers to understand model behavior and business logicSuggest solutions to identified product problemsIdentify problem areas and add them to a tracking systemInvestigate product quality to drive improvements and achieve better customer satisfactionMaintain test documentation Requirements: BS in Computer Science or related fieldExperience in QA AutomationProgramming skills in Python (preferred), JavaScript, or JavaExperience with test automation frameworks like PyTest, Robot Framework, Selenium, or CypressUnderstanding of the machine learning lifecycle and model testing conceptsFamiliarity with API testing tools (e.g., Postman) and CI/CD tools (e.g., Jenkins, GitHub)Experience with data validation in structured and semi-structured formats (JSON, CSV, etc.)Experience working with logs, metrics, and monitoring systems (e.g., Grafana)Familiarity with ML libraries from a testing perspectiveExperience testing systems involving real-time data, predictive modeling, or sentiment/emotion analysis