Quality Engineer

global leader in financial technology, powering mission‑critical systems that help the world pay, bank, and invest. If you’re passionate about advancing the future of fintech and building transformative products used across the money lifecycle, we’d like to ask you 

About the team: 

Revenue Connect is next-generation cloud platform, designed to transform commercial pricing and billing for banks with modern AWS infrastructure, API-first microservices, and advanced UI/UX. This approach eliminates legacy technical debt and delivers the speed, accuracy, and transparency banks demand. 

As Quality Engineering Lead, you’ll drive uncompromising reliability and intelligent automation, using AI-powered insights and automation-first engineering to ensure our platform’s excellence and robust validation at every layer. 

What you will be doing: 

• Design, build, and maintain automation frameworks for UI, API, data, and microservices testing. 

• Leverage AI-driven techniques for test generation, defect detection, and regression selection. 

• Develop and track observability-based quality metrics and CI/CD quality gates. 

• Validate AI/ML-powered features and collaborate to ensure safe and correct model behavior. 

• Define and implement end-to-end quality strategies across key platform components. 

• Partner with engineering, product, and SRE teams to ensure strong SDLC testability and guardrails. 

• Lead and mentor a quality engineering team, fostering AI-assisted engineering practices. 

• Drive technical excellence through standards, code reviews, and continuous skill development. 

What you will need: 

• Bachelor's degree in Computer Science or equivalent experience.  

• 7+ years of direct experience in software quality engineering, including automation architecture and framework development.  

• Strong hands‑on expertise with modern automation tools (Playwright, Selenium, JUnit/TestNG, Postman et al).  

• Proven experience validating AI‑enabled features — LLMs, anomaly detection, model‑driven logic, or generative workflows.  

• Deep knowledge of testing for distributed systems, microservices, data pipelines, and cloud environments (AWS preferred).  

• Solid experience integrating automation into CI/CD pipelines.  

• Strong debugging, problem‑solving, and analytical skills across frontend, backend, and data layers.  

• Excellent communication, collaboration, and cross‑team influence skills in a fast‑paced engineering organization. 

Added bonus if you have:  

• Experience with RAG pipelines, vector stores, guardrail frameworks, model evaluation, or safe AI patterns.  

• Experience testing financial systems, billing engines, or analytics‑heavy platforms.  

• Familiarity with synthetic data generation or statistical validation for AI and data‑driven testing. 

What we offer you:  

you can learn, grow and make an impact in your career. Our benefits include: 

Flexible and creative work environment 

Diverse and collaborative atmosphere 

Professional and personal development resources 

Opportunities to volunteer and support charities 

Competitive salary and benefits 

• Bachelor's degree in Computer Science or equivalent experience.  

• 7+ years of direct experience in software quality engineering, including automation architecture and framework development.  

• Strong hands‑on expertise with modern automation tools (Playwright, Selenium, JUnit/TestNG, Postman et al).  

• Proven experience validating AI‑enabled features — LLMs, anomaly detection, model‑driven logic, or generative workflows.  

• Deep knowledge of testing for distributed systems, microservices, data pipelines, and cloud environments (AWS preferred).  

• Solid experience integrating automation into CI/CD pipelines.  

• Strong debugging, problem‑solving, and analytical skills across frontend, backend, and data layers.  

• Excellent communication, collaboration, and cross‑team influence skills in a fast‑paced engineering organization. 

Added bonus if you have:  

• Experience with RAG pipelines, vector stores, guardrail frameworks, model evaluation, or safe AI patterns.  

• Experience testing financial systems, billing engines, or analytics‑heavy platforms.  

• Familiarity with synthetic data generation or statistical validation for AI and data‑driven testing.