- Location
- Bengaluru, KA,IN, IN
- Type
- Full-time
- Department
- Engineering
- Experience
- 5+ years
- Source
- Eightfold
Description
Design, develop, and maintain scalable test automation frameworks, libraries, shared services, and developer tools. Build reusable capabilities for browser, API, integration, contract, performance, accessibility, and end-to-end testing. Apply engineering standards for automation architecture, code quality, testability, reliability, and maintainability. Integrate automated quality checks, release validations, and quality gates into CI/CD pipelines. Improve test execution speed, parallelization, diagnostics, stability, and feedback time. Contribute logging, metrics, traces, dashboards, and alerts to improve automation observability. Analyze test, build, deployment, application, and production telemetry to identify quality issues. Partner with development, DevOps, SRE, security, architecture, and release teams to improve delivery quality. Support and mentor engineers and manual testers in automation, software development, DevOps, and AI-assisted engineering. Contribute to common platforms and reusable engineering patterns that reduce duplicated frameworks and maintenance effort. 5+ years of experience in software engineering, quality engineering, DevOps, platform engineering, test automation, or a related discipline. Strong programming skills in TypeScript, JavaScript, Python, Java, C#, or a comparable language. Experience designing or significantly contributing to maintainable automation frameworks or internal engineering tools. Hands-on experience with Playwright, Selenium WebDriver, or similar browser automation technologies. Experience with API, integration, contract, and end-to-end testing. Experience with Azure DevOps, GitHub Actions, Jenkins, or another CI/CD platform. Practical knowledge of Git, pull requests, code reviews, build systems, and deployment workflows. Working experience with Docker, Kubernetes, cloud services, or modern application architectures. Knowledge of observability concepts, including logging, metrics, tracing, dashboards, and failure diagnostics. Hands-on experience using Claude Code, Codex, or comparable AI coding assistants for software engineering work. Working understanding of MCP, tool calling, and agentic application-development concepts. Ability to review, validate, and safely use AI-generated code and automated workflows. Experience contributing to shared engineering platforms or developer-productivity tools. Experience with OpenTelemetry, Azure Monitor, Application Insights, Datadog, Grafana, or Splunk. Knowledge of performance, security, accessibility, resiliency, or production validation. Familiarity with retrieval-augmented generation, vector search, AI evaluation, or multi-agent systems. Experience helping teams transition from manual testing or tool-specific automation to reusable engineering solutions. What Success Looks Like Teams adopt shared automation capabilities and reusable engineering patterns. CI/CD feedback becomes faster, more reliable, and more actionable. Test flakiness, duplicated effort, and automation maintenance are reduced. Automation components are well designed, documented, observable, and reusable. AI-assisted development improves engineering productivity and failure analysis. AI-enabled workflows operate with appropriate validation, security, governance, and human oversight. Other team members develop stronger automation, programming, DevOps, and AI-engineering skills. The strongest candidate will think like a software engineer who specializes in quality. They should be able to demonstrate meaningful systems, frameworks, or engineering capabilities they have designed and implemented—not only tools they have used.
