Software Engineer, AI Engineer (Applied​/Software), AI QA ​/ Validation 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.

• 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, Git Hub Copilot, Claude, OpenAI, and related tools.
• Support testing and validation of scalable deployments across Docker, Kubernetes, and cloud-native environments.
• Collaborate with developers, architects, Dev Ops teams, and business stakeholders to ensure production readiness.
• Support continuous testing within Azure Dev Ops/Git Hub CI/CD pipelines.
• Drive observability, logging validation, monitoring validation, and runtime issue analysis for AI workloads.

• AWS Bedrock
• Amazon Q Developer
• AWS Kiro/Cairo
• Understanding of AI agents
• Lang Chain/Lang Graph

• 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, Git Hub Actions

• Understanding of IAM/security concepts
• AI guardrails
• Runtime monitoring and observability

• 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.