Lead QA Engineer

Overview

Lead a team of 4–8 QA and automation engineers to own quality for a platform that ingests assets, findings, and risk signals from third-party security vendors and normalizes them into a single risk model. This is a hands-on leadership role spanning test strategy, release decisions, and framework development, with a focus on AI-assisted, self-healing automation.

What you'll do

• Lead, mentor, and grow a team of 4–8 QA and automation engineers, including hiring, goal-setting, code reviews, and career development.
• Define and own the end-to-end test strategy for connector and platform releases, including test planning, risk assessment, coverage targets, and entry/exit criteria.
• Act as the quality voice in sprint planning, design reviews, and release readiness, making clear go/no-go recommendations backed by data.
• 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 with development, product, support, and sustenance teams to shift quality left and reduce customer-reported issues.
• Architect and evolve automation frameworks across React/TypeScript front ends, Java/Spring Boot microservices, REST/GraphQL APIs, and Kafka-based pipelines.
• Build resilient UI automation with Playwright or Selenium 4, including parallel execution and reliable test data management.
• Own API and contract testing (REST Assured, Pact, Postman/Newman) covering OAuth2, JWT, token, and Basic auth flows, pagination, throttling, and vendor error handling.
• Validate data end-to-end from ingestion through normalization, PostgreSQL persistence, aggregation, and UI, including schema and migration regression.
• Build mock vendor APIs and record-replay harnesses so connector tests run without live third-party credentials.
• Own CI/CD quality gates in Jenkins or GitHub Actions with fast PR-level feedback and tiered regression runs.
• Lead the design and rollout of self-healing automation, including automatic locator recovery, DOM-similarity scoring, element fingerprinting, and confidence-based fallback.
• 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.
• Introduce risk-based and change-impact test selection so CI runs the tests most relevant to each change.
• Evaluate AI testing tools (Healenium, Applitools, mabl, Testim, or in-house builds) and decide build-vs-buy based on accuracy, cost, and maintainability.
• Set guardrails for AI use in QA, including validating AI-generated tests, measuring healing accuracy, and preventing false passes.

What you'll need

• 5–8 years of experience in QA or test engineering.
• At least 2 years leading or mentoring a QA team.
• 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.
• Playwright and/or Selenium 4; TestNG or JUnit 5; Cypress, Jest, or Vitest.
• API automation: REST Assured, Postman/Newman, GraphQL testing, schema validation.
• Contract testing (Pact), service virtualization, and mocking (WireMock or similar).
• Performance testing with JMeter, k6, or Gatling.
• Visual regression and accessibility testing.
• Strong Java (17+) and JavaScript/TypeScript.
• SQL and data validation on PostgreSQL or similar.
• Familiarity with microservices, Spring Boot, and Kafka or other event streaming systems.
• Self-healing locator strategies and ML-based element matching.
• LLM integration: prompt design, structured outputs, and embedding model calls in test tooling.
• AI-assisted test generation, failure clustering, and log/trace summarization.
• CI/CD with Jenkins or GitHub Actions.
• Docker and Kubernetes fundamentals.
• Test management tools (Jira/Xray, TestRail, or Zephyr).
• Observability tools (Grafana, Prometheus, ELK) for test and pipeline diagnostics.
• Test strategy and planning for complex, multi-component releases.
• People leadership: hiring, mentoring, performance feedback.
• Clear communication with engineering, product, and leadership stakeholders.
• Data-driven decision-making on quality and release readiness.

Nice to have

• Python.
• Familiarity with RAG or vector search over test artifacts.
• Working knowledge of AI testing platforms (Healenium, Applitools, mabl, Testim).
• Background in cybersecurity: vulnerability management, CSPM, EDR, SIEM, or identity.
• Experience testing multi-tenant SaaS at large data volumes.
• ISTQB Advanced / Test Manager certification.
• Open-source contributions to testing or automation projects.

Details

• Location: Pune.