Quality Engineering Lead AI-Native SDLC

About Chubb Chubb is a world leader in insurance. With operations in 54 countries and territories, Chubb provides commercial and personal property and casualty insurance, personal accident and supplemental health insurance, reinsurance and life insurance to a diverse group of clients. Chubb Limited is listed on the NYSE (CB) and employs approximately 40,000 people worldwide. About Chubb India At Chubb India, we are in the middle of a genuine transformation not a gradual evolution, but a fundamental shift in how we build software. Our engineering teams are AI-native: developers use Claude Code to generate the vast majority of production code, unit tests, and test coverage Playwright UI tests, RestAssured API tests, Pact contract tests, K6 load tests, and TestContainers-based integration tests. Quality Engineering must change with it. We are looking for someone who sees AI-assisted development as an opportunity to redefine quality assurance entirely. Role: Quality Engineering Lead AI-Native SDLC Location: Hyderabad / Bengaluru Work From Office Experience: 12+ years Type: Full-time | Reports to: Engineering Lead The Mindset We're Hiring For This is first and foremost a mindset hire. The candidates we want to talk to: See developers generating tests as a good thing aningful " Think like an investigator: trace failures across services, Kafka messages, databases not just report UI errors Understand distributed systems, not just interfa Think like an adversary: probe AI-generated code for edge cases, error paths, business-rule violations Are hands-on by default they open test files, Measure quality in outcomes: mutation score, MTTD, defect escape rate not test case count Embrace AI tooling immediately Claude Code, prems Can make the case to engineers, not just managers Key Responsibilities Quality Strategy & Governance Define and own quality engineering strategy across all products Design and enforce tiered quality gates in CI/CDext Own release readiness decisions based on real risk, not just suite pass/fail Define metrics that matter: mutation score, defeate AI Output Validation Validate AI-generated tests for correctness, relevance, and coverage integrity Design and maintain test oracle libraries formtem behaviour Run mutation testing against AI-generated suites Identify and close systematic blind spots in AI- Hands-On Technical Leadership Use Claude Code daily. Build, iterate, and share prompt libraries for test generation Architect quality engineering infrastructure: teness detection Write, review, and refactor test code this is not a delegated activity QA-Owned Test Suites Regression suites: fast, reliable, trusted a g Smoke suites: automated gate for progressive rollout and rollback trigger Chaos tests: real failure injection Kafka failream timeouts Distributed System Investigation Know the full architecture: every service, Kafka topic, downstream dependency, database Lead root-cause diagnosis across system layers Build observability tooling: OpenTelemetry, Jaeger, structured logging, Kafka lag monitoring Adversarial & Exploratory Testing Hunt for failure modes nobody else thought to te conditions, contract drift Apply the professional hacker mindset to every release Skills & Qualifications Non-Negotiable 12+ years in software quality engineering with technical leadership progression Automation-first manual testing background is Hands-on with UI (Playwright/Selenium/Cypress), API (RestAssured/Karate), contract (Pact), and load (K6/JMeter) testing Programming proficiency in Java, Python, or Type CI/CD fluency: quality gates, rollback triggers, pipeline architecture Distributed systems literacy: microservices, Kaf Critical thinking on AI-generated tests not just "does it compile " High Importance Prompt engineering for test generation; Claude Cgnal Mutation testing (Pitest, Stryker) Chaos engineering (Chaos Monkey, Gremlin, Litmus Observability stack: OpenTelemetry, Jaeger, structured logging TestContainers and infrastructure-layer integrat Test data management: synthetic data, GDPR-safe masking Valuable Pact contract testing in microservices Performance/load testing (K6, Gatling, JMeter) Event-driven system testing: Kafka end-to-end va Security testing beyond OWASP: threat modelling, adversarial inputs What Success Looks Like in Year One QA is seen as a technical peer, not a gating function Automated smoke gates and rollback in every depl A trusted regression suite that runs on every PR a green run means something At least one chaos suite covering real distribut Production incidents diagnosed at the system layer, not the UI layer Developers generating, validating, and owning AI The team measuring mutation score, MTTD, and defect escape rate not test Abou