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Niche senior QA role with specialized LLM and AI evaluation skills reduces applicant density.
Highly specialized AI/LLM testing and cybersecurity context makes cross-industry transferability limited.
Explicit 10+ years requirement plus deep AI, testing, and cloud tool mandates make filters stringent.
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Build and maintain scalable automated testing frameworks and CI/CD pipelines for AI, machine learning, cybersecurity, and distributed cloud-native systems.
Design and execute AI-specific validation and evaluation pipelines to measure performance, reliability, hallucination rates, bias, latency, and other quality metrics for generative AI and large language model applications.
Collaborate with product and engineering teams to set AI quality standards, testing strategies, and improve automation and testing methodologies across complex microservices environments.
10+ years of experience delivering high-quality software solutions.
Hands-on experience with testing generative AI, machine learning, and large language model applications including AI evaluation frameworks like DeepEval, Ragas, Promptfoo, LangSmith, or OpenAI Evals.
Experience building automation frameworks for functional, load, and fuzz testing, and deploying services on cloud platforms such as AWS, Azure, or GCP.
Work Experience Required: More than 10 years. No explicit mention of mandatory degrees, location, or notice period.
Strong background in combining AI/ML expertise with software quality engineering in distributed cloud-native environments involving microservices architectures.
Experience architecting end-to-end automated AI validation pipelines capable of measuring nuanced metrics like hallucination detection, model drift, and safety risks for large language models.
Proven ability to work with cross-functional teams to define AI quality standards and implement improvements that increase reliability, safety, and deployment confidence for high-throughput cybersecurity platforms.