Pioneer the future of autonomous testing by designing and implementing intelligent test systems across all levels (unit, integration, end-to-end), becoming the go-to expert on AI-driven testing procedures, agent orchestration, and risk mitigation in cutting-edge applications.
Shape groundbreaking test strategies for AI systems: Take ownership of test specifications for LLM-powered applications, architect innovative prompt validation frameworks, and establish observability practices that push the boundaries of quality assurance.
Engineer next-generation test solutions using cutting-edge tools like Playwright, Stagehand, Locust, and Allure, seamlessly integrated with LLM orchestration frameworks to create self-healing, adaptive test systems that evolve alongside your applications.
Build and command fleets of intelligent SDET Agents that revolutionize testing workflows—agents that generate their own test cases, dynamically validate prompts, and autonomously triage defects with unprecedented efficiency.
Orchestrate the complete lifecycle of AI application testing: Lead autonomous test execution, harness AI-driven defect detection and classification, unlock actionable insights through LLM-powered analysis, and establish world-class observability practices.
Drive innovation through collaboration: Partner closely with AI/ML engineers, prompt engineers, and developers to ensure bulletproof testing of intelligent systems, pushing reliability and performance to new heights in unexplored territory.
Build the knowledge foundation for tomorrow: Document test cases for LLM behaviors and agent interaction patterns with crystal clarity, creating a knowledge base that empowers your team and advances the state of the art in AI testing.
What do you need to succeed?
Must Haves:
3+ years of Quality Engineering experience in diverse environments (cloud, distributed, APIs, databases, AI/ML systems), with proven ability to resolve complex cross-functional issues in agent-based architectures.
3–5 years of hands-on automation experience designing efficient test strategies for autonomous systems, reducing manual testing through intelligent agents, and covering component, integration, and end-to-end scenarios.
Deep expertise in testing AI agent applications: Proficiency in testing LLM-based agents, multi-agent systems, prompt engineering validation, output consistency testing, and handling non-deterministic behavior.
AI framework proficiency: Hands-on experience with LangChain, LangGraph, CrewAI, AutoGen, or similar frameworks for building and testing agent workflows.
DevOps proficiency in CI/CD, GitHub Actions, JIRA, and qTest, with experience integrating AI model evaluation into deployment pipelines.
Automation tools expertise in Playwright, Locust, Python httpx, and AI-specific evaluation tools like promptfoo
Programming skills in Python, TypeScript, SQL, and source control tools (Git), with strong understanding of asynchronous programming for agent orchestration.
Experience in Agile/Iterative development, defining test strategies for AI systems, evaluating LLM outputs, and reviewing artifacts (agent code, prompt templates, automation scripts).
Linked-in
Google