SDET - II

As an SDET II, you will be responsible for building the test infrastructure, automation harnesses, and engineering workflows that allow our teams and AI coding agents to safely understand, modify, test, and ship code. This is not a manual QA role. This is an engineering-heavy SDET role focused on test automation, system reliability, developer productivity, and AI-agent readiness. You will work closely with SDEs, engineering leads, and platform teams to make our codebase more testable, observable, and agent-friendly. You will design harnesses that allow AI agents and developers to run reliable checks, validate changes, reproduce bugs, generate tests, and catch regressions before production.

Responsibilities:

• Build Test Harnesses for AI Agents: Design and build automated test harnesses that allow AI coding agents to safely make and validate code changes.
• Own Automation Infrastructure: Build and maintain automation suites across backend services, APIs, web flows, and critical business journeys.
• Enable AI-First Engineering Workflows: Work with tools like Cursor, GitHub Copilot, Claude Code, Codex, or similar AI coding agents to improve engineering velocity.
• Developer Productivity: Build internal tools that reduce developer toil around testing, debugging, and release validation.

Requirements:

• 4+ years of hands-on experience as an SDET, automation engineer, test infrastructure engineer, or software engineer with strong testing ownership.
• Strong programming ability in at least one backend language such as Java, Kotlin, TypeScript, JavaScript, Python, or Go.
• Experience testing REST APIs, backend services, databases, and distributed systems.
• Hands-on experience with automation tools such as Playwright, Cypress, Selenium, REST Assured, Postman/Newman, Jest, JUnit, TestNG, or similar.
• Good understanding of test design: unit tests, integration tests, contract tests, API tests, end-to-end tests, mocks, fixtures, and test data management.
• High ownership and bias for execution: you should be comfortable taking an ambiguous quality problem and converting it into working infrastructure.
• Good written and verbal communication.
• Willingness to adopt AI-powered engineering tools and apply them pragmatically to improve test coverage, development speed, and release confidence.