Job Description: Role Summary
We are seeking a self-driven 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, 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 K6, 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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