Qualys
From Qualys's careers page
Status: listed on the employer's own careers page when we last checked (daily). Confirm on their site before applying.
You will act as a senior individual contributor and technical mentor for QA and automation engineers.
Come work at a place where innovation and teamwork come together to support the most exciting missions in the world!
Qualys is looking for a Lead QA Engineer to own quality for products in the Cybersecurity and Compliance domain used by thousands of enterprise customers.
You will act as a senior individual contributor and technical mentor for QA and automation engineers. You will define the test strategy across UI, backend, and data pipelines, and drive the move toward AI-assisted, self-healing automation. This is a hands-on technical role: you set quality direction, influence release decisions, and actively write and review framework code. Candidates who have built or meaningfully enhanced AI-based self-healing automation tools will stand out.
QA Mentorship & Strategy
Mentor and guide QA and automation engineers through technical coaching, code reviews, and knowledge sharing.
Define and own the end-to-end test strategy for legacy components by improving automation coverage, while ensuring all new components are fully automated from the start.
Act as the quality voice in sprint planning, design reviews, and release readiness; make data-backed go/no-go recommendations.
Establish scalable QA processes: defect triage, severity/priority standards, regression tiers, test case management, and root-cause analysis for escaped defects.
Track and report quality metrics (defect leakage, automation coverage, flaky-test rate, mean time to detect, release cycle time) to engineering and product leadership.
Partner closely with Development, Product, Support, and Sustenance teams to shift quality left and reduce customer-reported issues.
Test Automation (Hands-On)
Architect and evolve automation frameworks across React/TypeScript front ends, Java/Spring Boot microservices, REST APIs, and Kafka-based pipelines.
Own API and contract testing (REST Assured, Pact, Postman/Newman) covering OAuth2, JWT, token, and Basic auth flows, pagination, throttling, and vendor error handling.
Own CI/CD quality gates in Jenkins or GitHub Actions with fast PR-level feedback and tiered regression runs.
AI-Driven & Self-Healing Automation
Lead the design and rollout of self-healing automation: automatic locator recovery, DOM-similarity scoring, element fingerprinting, and confidence-based fallback that keeps suites green through UI changes.
Apply LLMs across the QA lifecycle — generating test cases from requirements and API specs, converting manual tests to automated scripts, summarizing failures, and suggesting root causes from logs.
Build intelligent flaky-test detection, failure classification, and auto-quarantine to separate real regressions from environmental noise.
Set guardrails for AI use in QA — validating AI-generated tests, measuring healing accuracy, and preventing false passes.
8-10+ years in QA / test engineering, with at least 2 years leading or mentoring a QA team.
Proven track record of building automation frameworks from scratch and owning release quality for a SaaS product.
Hands-on experience building, extending, or productionizing an AI-based or self-healing automation solution, with the ability to explain how you measured its impact.
Automation & Testing
Robot, Playwright and/or Selenium 4; TestNG or JUnit 5; Cypress, Jest, or Vitest
API automation: REST Assured, Postman/Newman, schema validation
Contract testing (Pact), service virtualization, and mocking (WireMock or similar)
Performance testing with JMeter, k6, or Gatling
Visual regression and accessibility testing
Programming & Backend
Python, Java(17+) and JavaScript/TypeScript
SQL and data validation on Oracle or similar
Familiarity with microservices, Spring Boot, and Kafka (or other event streaming systems)
AI in QA
Self-healing locator strategies and intelligent element matching
LLM integration: prompt design, structured outputs, and embedding model calls into test tooling
AI-assisted test generation, failure clustering, and log/trace summarization
Familiarity with RAG or vector search over test artifacts is a plus
Working knowledge of AI testing platforms (Healenium, Applitools, mabl, Testim)
DevOps & Tooling
CI/CD with Jenkins or GitHub Actions
Docker and Kubernetes fundamentals
Test management (Jira/Xray, TestRail, or Zephyr)
Observability tools (Grafana, Prometheus, ELK) for test and pipeline diagnostics
Technical Mentorship
Strong ability to mentor engineers through technical guidance and code reviews
Clear communication with engineering, product, and leadership stakeholders
Data-driven decision-making on quality and release readiness
Background in cybersecurity — vulnerability management, Policy Compliance
Experience testing multi-tenant SaaS at large data volumes.
Open-source contributions to testing or automation projects.
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