Software Development Engineer in Test III

Job Description Role Overview: As a SDET III, you will play a crucial role as a bridge between development and quality by utilizing cutting-edge Generative AI and Autonomous Testing tools to enhance release cycles. Your responsibilities will go beyond traditional testing as you will be involved in constructing intelligent systems that oversee, diagnose, and enhance the testing lifecycle. Key Responsibilities: - AI-Driven Framework Development: - Design and maintain scalable automation frameworks (Java/Python) integrated with AI agents for self-healing scripts and autonomous test generation. - Prompt Engineering for QA: - Utilize LLMs (GitHub Copilot, Gemini) to generate complex test data, synthetic datasets, and boilerplate automation code. - Creating network simulations (E2E Integration tests): - Develop strategies for validating E2E integrations, including a network simulator accommodating different datasets for multiple customers to predict if all integrations work as expected. - Shift-Left & Observability: - Integrate testing into CI/CD pipelines (Github Actions/GitLab/Jenkins) and utilize production observability (logs/metrics) to inform test design. - Agentic Workflows: - Orchestrate multi-agent AI systems capable of autonomously exploring the UI, discovering edge cases, and reporting bugs without human intervention. Required Qualifications: - Experience: - 6-9 years in Software Engineering/SDET roles, with at least 12 years specializing in AI/ML-based automation. - Coding Mastery: - Deep proficiency in at least one object-oriented language and experience with 'Pipeline as Code.' - Problem Solving: - Ability to debug complex distributed systems and microservices architectures. - Education: - B.S./M.S. in Computer Science, Data Science, or a related technical field. Additional Information: The job requires a strategic thinker who can evaluate product 'Quality of Experience' beyond traditional 'Pass/Fail' metrics. Adaptability is key, with a mindset geared towards continuous learning to keep pace with the evolving AI tooling landscape. Effective communication skills are essential to articulate technical risks of AI outputs to non-technical stakeholders. Role Overview: As a SDET III, you will play a crucial role as a bridge between development and quality by utilizing cutting-edge Generative AI and Autonomous Testing tools to enhance release cycles. Your responsibilities will go beyond traditional testing as you will be involved in constructing intelligent systems that oversee, diagnose, and enhance the testing lifecycle. Key Responsibilities: - AI-Driven Framework Development: - Design and maintain scalable automation frameworks (Java/Python) integrated with AI agents for self-healing scripts and autonomous test generation. - Prompt Engineering for QA: - Utilize LLMs (GitHub Copilot, Gemini) to generate complex test data, synthetic datasets, and boilerplate automation code. - Creating network simulations (E2E Integration tests): - Develop strategies for validating E2E integrations, including a network simulator accommodating different datasets for multiple customers to predict if all integrations work as expected. - Shift-Left & Observability: - Integrate testing into CI/CD pipelines (Github Actions/GitLab/Jenkins) and utilize production observability (logs/metrics) to inform test design. - Agentic Workflows: - Orchestrate multi-agent AI systems capable of autonomously exploring the UI, discovering edge cases, and reporting bugs without human intervention. Required Qualifications: - Experience: - 6-9 years in Software Engineering/SDET roles, with at least 12 years specializing in AI/ML-based automation. - Coding Mastery: - Deep proficiency in at least one object-oriented language and experience with 'Pipeline as Code.' - Problem Solving: - Ability to debug complex distributed systems and microservices architectures. - Education: - B.S./M.S. in Computer Science, Data Science, or a related technical field. Additional Information: The job requires a strategic thinker who can evaluate product 'Quality of Experience' beyond traditional 'Pass/Fail' metrics. Adaptability is key, with a mindset geared towards continuous learning to keep pace with the evolving AI tooling landscape. Effective communication skills are essential to articulate technical risks of AI outputs to non-technical stakeholders.