Test Automation & AI Prompt Engineering (SDET - C# or Java)

Description

• Integrate AI-assisted testing workflows seamlessly into existing CI/CD pipelines (GitLab, Jenkins, Azure DevOps, etc.).
• Ensure high-quality, stable test automation aligned with development best practices.
• Review, validate, and refine AI-generated test scripts for correctness, maintainability, and robustness.
• Analyze large and complex codebases to identify testing needs, edge cases, and coverage gaps.
• Establish guidelines and best practices for safe and effective use of AI in software testing.
• Collaborate with DevOps teams to ensure scalable, secure, and maintainable infrastructure for AI-backed test automation.
• Set up and manage on-premise AI model deployments, vector stores, and related tooling to support code-analysis and test generation workflows.
• Design, develop, and maintain automated white-box test suites for diverse technologies.
• Use AI tools/models to generate test cases, test scripts, and supporting documentation.
• Troubleshoot, debug, and optimize both test automation pipelines and underlying code issues.
• Apply advanced prompt engineering and context-setting techniques along with feedback loops to maximize AI model effectiveness.

Requirements

• Hands-on experience using leading AI coding assistants such as GitHub Copilot, OpenAI models, CodeWhisperer, or similar.
• Proactive, self-driven, and able to operate independently with AI-augmented workflows.
• Proficient in crafting prompts, managing context, and using model-specific techniques to generate meaningful output.
• Strong developer mindset with attention to detail, logical problem-solving, and code quality.
• Solid understanding of software engineering concepts, debugging, and code review.
• Knowledge of testing tools such as pytest, nUnit, Playwright, Selenium, or API testing frameworks.
• Experience building automation frameworks and writing automated tests (unit, integration, API, or functional).
• Experience using LLM-based tools for code analysis, summarization, test generation, and script creation.
• Prior experience in tech product companies or automation-focused engineering roles.
• Strong programming skills (C# or Java)and scripting skills (e.g. Python, Shell).
• Exposure to vector databases or retrieval-augmented generation (RAG) setups.
• Familiarity with white-box testing approaches, code instrumentation, and static/dynamic code analysis.
• Experience deploying and managing AI/LLM models on-premise or in self-hosted environments.
• Understanding of model limitations, failure modes, and refinement strategies.
• Comfortable analyzing complex systems and learning new domains quickly.
• Experience working with microservices, distributed systems, or large monolithic codebases.
• Hands-on experience with CI/CD tools and pipeline integrations.

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance