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Mid-level QA role with common title, metro location, and broad skill requirements drives high competition.
Core QA and automation skills are transferable, though AI and cloud-specific testing needs increase domain sensitivity.
Explicit 4-9 years plus many mandatory automation, cloud, and tooling skills creates strict shortlisting.
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Lead backend engineering for AI team focusing on performance, scalability, and efficiency of backend systems.
Design and implement end-to-end test strategies including functional, non-functional, API, and performance testing with automation and CI/CD pipelines.
Research and apply advanced AI models and system optimizations to improve production monitoring, inference costs, and multi-agent workflows.
4-9 years of relevant experience in backend testing and engineering.
Strong expertise in test strategies including functional, integration, API, regression, and non-functional testing.
Proficiency with automation tools like Playwright/Selenium/Cypress and programming languages including Python, Java, JavaScript or TypeScript.
Experience with microservices, cloud platforms (AWS/Azure/GCP), containerization (Docker/Kubernetes), CI/CD, and monitoring tools (Grafana, CloudWatch, ELK).
Experienced backend engineer with leadership exposure in AI or complex distributed backend systems.
Hands-on expertise in test automation and quality engineering with knowledge of modern testing frameworks, tools, and cloud-native architectures.
Familiarity with AI model deployment, advanced testing of AI/LLM systems, and performance optimization techniques for scalable production environments.