Software Development Engineer

Develop and maintain automated tests, framework components, libraries, and engineering utilities. Build automation for browser, API, integration, contract, accessibility, and end-to-end testing. Write clean, reliable, maintainable, and well-tested code. Follow established standards for automation design, code quality, testability, and reliability. Integrate automated tests and quality checks into CI/CD pipelines. Investigate test failures, flaky automation, environmental issues, and product defects. Improve test execution, diagnostics, stability, and feedback time. Add logging, metrics, reports, and other diagnostic information to automation solutions. Collaborate with development, DevOps, product, and quality-engineering team members. Use AI coding assistants to support coding, automation development, debugging, documentation, and analysis. Contribute to shared platforms and reusable engineering components instead of creating isolated solutions. Participate in code reviews, design discussions, technical learning, and continuous-improvement activities. 2+ years of experience in software development, quality engineering, test automation, DevOps, or a related technical role. Programming experience in TypeScript, JavaScript, Python, Java, C#, or a comparable language. Understanding of software-development fundamentals, object-oriented or modular design, data structures, debugging, and automated testing. Hands-on experience with Playwright, Selenium WebDriver, Cypress, or a similar browser automation technology. Familiarity with integration, regression, and end-to-end testing concepts. Working knowledge of Git, pull requests, code reviews, build systems, and development workflows. Exposure to Azure DevOps, GitHub Actions, Jenkins, or another CI/CD platform. Basic knowledge of Docker, cloud services, or modern web-application architectures. Understanding of logging, test reporting, and failure diagnostics. Experience using Claude Code, Codex, or a comparable AI coding assistant for development or automation work. Familiarity with AI tool calling, MCP, or agentic-development concepts, or a demonstrated ability to learn them. Strong problem-solving, communication, collaboration, and learning skills. Experience contributing to automation frameworks, shared libraries, or developer tools. Familiarity with Kubernetes, cloud platforms, or containerized test execution. Exposure to observability platforms such as Azure Monitor, Application Insights, Datadog, Grafana, or Splunk. Knowledge of performance, security, accessibility, or resiliency testing. Familiarity with AI application development, retrieval-augmented generation, structured outputs, or AI evaluation. What Success Looks Like Reliable automation is delivered as part of regular product-development work. Automated tests provide fast and useful feedback through CI/CD pipelines. Test failures are diagnosed effectively, and flaky tests are reduced. Automation code is maintainable, reusable, documented, and aligned with shared standards. AI coding assistants are used responsibly to improve productivity and software quality. The engineer progressively develops stronger skills in automation architecture, DevOps, observability, and agentic AI. Contributions help reduce manual testing effort and duplicated automation across teams. The strongest candidate will have a software engineering mindset, a strong interest in quality, and the ability to learn new technologies quickly. They should be able to demonstrate code, automation, or engineering solutions they have personally built—not only testing tools they have operated.