qa engineer (auto) for AI agent platforms

Intellias' client is a global enterprise investing in cloud, data, and AI capabilities. The project is building an enterprise-grade AI Agent Platform for developing, deploying, orchestrating, securing, and observing AI agents across multiple teams and use cases.

• Design and execute tests for agentic workflows, including tool discovery, tool invocation, and multi-step agent interactions;
• Validate MCP protocol behavior, including tool list aggregation, tool-call routing, and streaming responses;
• Test authorization and authentication controls, including unauthorized tool-access scenarios and policy-enforcement validation;
• Perform performance testing for agentic workloads, including tool-discovery latency, concurrent agent sessions, and agent-to-agent communication latency;
• Build and maintain automated tests in Python using pytest, including asynchronous testing, mock MCP servers, and agent workflow simulations;
• Validate agentic observability, including trace completeness, agent-step logging, and audit trails;
• Perform end-to-end testing across agent, gateway, and tool interaction flows;
• Identify reliability, security, and performance issues in complex agent workflows and support their resolution.

• 4+ Years of QA engineering experience for AI/ML or agent systems;
• Experience testing LLM agent workflows, including tool calling and multi-step reasoning;
• Experience with security testing for authorization and authentication;
• Experience with performance benchmarking for cloud services;
• Nice to have: MCP protocol testing experience, agent framework testing with LangChain, LangGraph, or Crew, AWS Bedrock Agents testing, chaos engineering for agentic systems, audit log validation with CloudTrail or OpenTelemetry traces.

• Benefits supporting employee well-being and professional growth are offered.