Software Development Engineer in Test (SDET II) with experience testing AI-powered applications, Generative AI solutions, and Agentic AI systems. This role is responsible for designing and executing quality engineering activities, developing automation solutions, validating APIs and integrations, supporting CI/CD and release readiness, and promoting a quality-first engineering mindset across traditional and AI-driven platforms.
Key Experience Requirements
• 8+ years of software quality engineering, test automation, or SDET experience in Agile development environments.
• Strong proficiency in Python, Pytest, Pandas, Dataframes and object-oriented programming principles.
• 6+ years of experience with UI automation frameworks such as Playwright or Selenium.
• 6+ years of experience testing RESTful APIs and microservices.
• Hands-on experience with CI/CD pipelines and DevOps practices.
• Experience working with SQL and NoSQL databases.
• Experience with AWS or other cloud-based platforms.
• Understanding prompt engineering, prompt validation, hallucination detection, bias testing, guardrail validation, and AI output evaluation.
• Knowledge of LLMs, Retrieval-Augmented Generation (RAG), AI agents, MCP, and AI-assisted workflows.
• Ability to develop QA strategies, test frameworks, and automation solutions for AI-driven applications.
• Analytical, problem-solving mindset with continuous learning and quality-first engineering habits.
Key Responsibilities
• Design and execute functional, integration, system, regression, and automation testing activities.
• Apply Shift-Left testing practices by participating in requirement reviews and defining test scenarios early in the SDLC.
• Build, maintain, and enhance UI automation frameworks using Python, PyTest, and Playwright.
• Perform manual and automated API testing using industry-standard tools and frameworks.
• Collaborate with developers, product owners, business analysts, and DevOps teams to ensure product quality.
• Work with SQL and NoSQL databases for backend validation and data testing.
• Support CI/CD pipelines and ensure automated tests are integrated into deployment processes.
• Create test plans, test cases, test data strategies, and quality metrics dashboards.
• Manage defect lifecycle activities and provide clear reporting of quality risks.
• Develop AI test datasets, benchmarks, evaluation frameworks, and automated validation approaches.
• Experience with performance and load testing using JMeter or similar tools.
• Knowledge of AI observability, model monitoring, and evaluation platforms.
• Experience with monitoring tools such as New Relic, Datadog, Splunk, or CloudWatch.
Preferred Qualifications
• Experience testing Generative AI and Agentic AI applications.
• Ability to validate AI outputs for accuracy, reliability, and business relevance.
• Knowledge of AI agent workflows, prompt testing, hallucination detection, and model evaluation.
• Understanding AI safety, guardrails, security, compliance, and responsible AI practices.
• Experience with LangChain, LangGraph, MCP, OpenAI, Anthropic, Azure OpenAI, Kiro or Amazon Bedrock.
• Familiarity with AI governance, risk management, and AI quality metrics.
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