Software Engineer

Role Summary We are looking for a highly skilled QA Engineer specializing in AI-enabled systems, intelligent automation, and enterprise AI validation. The role involves testing AI-native applications, AI agents, orchestration workflows, cloud-integrated systems, and AI-assisted SDLC solutions across multiple delivery tracks including CGS, SDS, ETL, modernization, APIs, cloud-native systems, and legacy platforms. The ideal candidate should possess strong expertise in AI testing strategies, automation frameworks, cloud-native testing, API validation, AI harness creation, and scalable deployment validation. Key Responsibilities Design and execute testing strategies for AI-native applications, AI agents, and enterprise AI workflows. Build AI testing harnesses and automated validation frameworks for LLM-based systems. Validate RAG pipelines, prompt flows, agent orchestration, APIs, and enterprise integrations. Perform functional, integration, regression, security, and performance testing for AI-enabled systems. Validate AI agent behavior, hallucination risks, guardrails, prompt responses, and orchestration reliability. Work with AI-assisted testing ecosystems including Amazon Q Developer, AWS Kiro/Cairo, GitHub Copilot, Claude, OpenAI, and related tools. Support testing and validation of scalable deployments across Docker, Kubernetes, and cloud-native environments. Collaborate with developers, architects, DevOps teams, and business stakeholders to ensure production readiness. Support continuous testing within Azure DevOps/GitHub CI/CD pipelines. Drive observability, logging validation, monitoring validation, and runtime issue analysis for AI workloads. Required Technical Skills AI & Automation Ecosystem AWS Bedrock Amazon Q Developer AWS Kiro/Cairo Understanding of AI agents LangChain/LangGraph QA & Automation Expertise Strong experience in API testing, Automation testing, Functional testing, Regression testing, Integration testing, Data validation Hands-on with Postman, SoapUI, SQL, Python Understanding of Docker containerization, GitHub Actions Security & Governance Understanding of IAM/security concepts AI guardrails Runtime monitoring and observability Mandatory Practical Experience Must demonstrate hands-on testing experience for AI-enabled enterprise applications and AI agents. Experience validating AI workflows, orchestration pipelines, and enterprise integrations. Must have worked on automation-heavy delivery environments and AI-assisted SDLC ecosystems. Strong troubleshooting, analytical, and stakeholder communication capabilities. High learning agility with ability to rapidly adapt to evolving AI ecosystems and enterprise technology stacks. #J-18808-Ljbffr