Software QA Engineer - GenAI/LLM (8 to 12 yrs)

Overview

Lead the quality strategy for large-scale Generative AI products and platforms within Cisco's AI Software & Platform team, which delivers AI products and platform for all Cisco Secure products and portfolios.

What you'll do

• Define how AI services, distributed systems, and end-to-end customer experiences are validated.
• Design scalable testing and automation approaches across AI pipelines, APIs, user interfaces, federated microservices, and multi-tenant SaaS architectures.
• Establish release-readiness practices using measurable quality gates, test traceability, production-representative environments, and data-driven Go/No-Go criteria.
• Analyze model degradation, system failures, and test instability to drive root-cause analysis and improve the reliability and resilience of the AI platform.
• Partner with Engineering, Product, UX, DevOps, Documentation, and Customer Engineering.
• Mentor QA engineers and advance quality-first engineering practices across teams.

What you'll need

• BS in Computer Science, Engineering, or a related field with 7+ years of relevant experience; or a Master's of Science Degree with 4+ years of related experience; or a PhD with 1+ years of related experience.
• Experience designing and implementing test strategies, automation frameworks, and test infrastructure for distributed software systems, cloud platforms, or SaaS products.
• Python development experience and experience with automated testing frameworks such as Pytest, Playwright, Selenium, or equivalent.
• Experience validating frontend and backend systems, including REST and/or GraphQL APIs, service integrations, CI/CD pipelines, and distributed microservice architectures.
• Experience defining release-quality criteria and diagnosing complex system failures using test results, logs, metrics, and other engineering data.

Nice to have

• Experience testing Generative AI, AI/ML platforms, AI agents, LLM-powered applications, or other AI-based products, including evaluation of model quality and degradation.
• Experience designing AI-agent-assisted QA workflows using tools such as Cursor, Claude, Codex CLI, or equivalent, including agent-based or MCP-based test orchestration.
• Experience testing multi-tenant cloud platforms and managing complex AI test environments involving datasets, model versions, feature flags, Kubernetes, and deployment technologies such as ArgoCD.
• Demonstrated experience building scalable test frameworks and establishing quality practices across multiple engineering teams, including mentoring QA or software engineers.
• Knowledge of security, privacy, data governance, or compliance requirements relevant to enterprise SaaS and AI products.

Details

• Location: Bangalore, India.