QA Automation Engineer

We're hiring a QA Automation Engineer to drive automation-first, AI-assisted testing across our backend platform. You'll spend most of your time building Playwright suites, designing AI agents, and shaping what quality looks like in an agent-driven SDLC with manual testing reserved only for exploratory and edge cases automation can't yet reach.

Responsibilities:

• Build and own automated test suites using Playwright for end-to-end, UI, and API testing across backend services and platform workflows.
• Use Claude Code (agents, skills, and MCP servers) as a daily driver generating test cases, test data, and edge-case scenarios from requirements, PRs, and production signals, shrinking test authoring time from days to hours.
• Design AI agents that own slices of the SDLC from PR-triggered test design to autonomous regression triage and release sign-off.
• Build custom Claude skills and MCP servers that integrate QA workflows with Jira, CI/CD pipelines, test data stores, ScyllaDB/Kafka inspection tools, and observability platforms.
• Apply AI-driven defect prediction, root cause analysis, and flaky test detection to focus effort where it matters most.
• Embed automated quality gates into CI/CD (GitHub Actions), including AI-powered code review, test impact analysis, and regression triggers to shift quality left.
• Validate datasets using SQL across transactional and analytical systems. Use targeted manual and exploratory testing only where automation gaps exist; convert recurring manual checks into automated coverage.

Requirements:

• 4+ years in QA with a clear bias toward automation and tooling over manual execution.
• Strong hands-on experience with Playwright (or equivalent modern framework, Cypress, etc. ) for UI and API automation.
• Proficiency in JavaScript/TypeScript and API testing with Postman/Insomnia.
• Working fluency in LLM-powered development workflows (Claude Code, Cursor, Copilot, or similar) for test generation, debugging, and review.
• Grasp of AI/ML fundamentals, prompt engineering, context management, and evaluating LLM outputs for correctness and reliability.
• Comfort in an agent-driven environment where AI agents are first-class collaborators across coding, testing, and operations.
• Treats AI agents as collaborators, not tools; knows when to trust, verify, or override their output.
• Solid SQL for data validation and a working understanding of API/backend test lifecycles.
• CI tooling experience (GitHub Actions, Jenkins).
• Agile/Scrum experience and clear async communication for remote, cross-functional work.

Nice to Have:

• Built MCP servers or custom Claude skills/tools to extend AI agent capabilities.
• Exposure to agentic test orchestration autonomous agents that plan, execute, and adapt test runs based on code or requirement changes.
• Familiarity with AI-based observability and anomaly detection for proactive regression monitoring.
• Experience measuring AI-assisted productivity metrics (test creation velocity, coverage uplift, and defect escape rate).
• Prior work on distributed systems, event-driven architecture, or B2B SaaS platforms.
• CI tooling experience (GitHub Actions, Jenkins).