Lead Software Engineer in Test – Agentic AI (Remote - US)

Job summary

We are a leader in specialty healthcare technology solutions. We’re committed to hiring and retaining talent, which is why we invest in our employees through competitive pay, a generous bonus structure, great healthcare, a comprehensive wellness program, and many other benefits.

Benefits

Qualifications

• Bachelor’s degree in Computer Science, MIS, Engineering, or a related technical field.
• 8+ years of experience in Software Development or Software Engineer in Test (SDET/SET) roles.5+ years of hands-on experience developing automation frameworks using JavaScript/TypeScript or Java.
• Strong experience with automation
• Experience with Azure DevOps, GitHub Actions, or similar CI/CD platforms.
• Experience with AI observability

Responsibilities

• The Lead Software Engineer in Test – Agentic AI is responsible for leading quality engineering strategy and execution across next-generation AI-powered applications and distributed systems.
• include:
• Design, develop, and maintain scalable automation frameworks and regression test suites across UI, API, backend, and AI-driven applications.
• Lead quality engineering initiatives for Agentic AI systems, including LLM workflows, RAG pipelines, autonomous agents, and distributed orchestration platforms.
• Validate complex AI agent behaviors such as multi-agent coordination, tool calling, prompt chaining, memory retention, conversational workflows, and fallback/retry mechanisms.
• Develop and execute functional, integration, regression, end-to-end, and performance testing strategies across cloud-native and distributed environments.
• Collaborate with Software Engineers, Product Managers, and DevOps teams to integrate testing throughout the software development lifecycle.
• Build reusable automation solutions to validate APIs, orchestration layers, asynchronous workflows, enterprise integrations, and AI-driven business processes.
• Design and maintain robust test environments, CI/CD integrations, cloud execution platforms, and automation pipelines to improve efficiency and reliability.
• Establish and drive best practices related to code quality, maintainability, scalability, dependency injection, observability, and testability.
• Lead validation efforts for AI workflow resiliency, distributed system reliability, data consistency, and orchestration performance under failure and edge-case conditions.
• Mentor QA engineers and contribute to the development of scalable QA standards, automation strategies, reporting practices, and continuous improvement initiatives.

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