Senior Engineering Manager, Kong

• If you don’t think you meet all of the criteria below but are still interested in the job, please apply. Nobody checks every box - we’re looking for candidates that are particularly strong in a few areas, and have some interest and capabilities in others
• 10+ Years in Software Engineering / SDET: Proven track record building and scaling enterprise software test automation systems
• 4+ Years in Engineering Management: Direct experience leading distributed or global software engineering/SDET teams
• Hands-on AI Integration Vision: Proven experience applying LLMs, Generative AI, or autonomous agent frameworks (e.g., Model Context Protocol, agentic tool-calling, synthetic data generation) to software quality or developer workflows
• Operational Excellence: Demonstrated ability to manage follow-the-sun operational cadence, vendor partnerships, and cross-timezone execution seamlessly

Desirable

• As the Senior Engineering Manager, you will serve as the global leader for Quality Engineering across Kong’s flagship runtime platforms (AI Gateway, API Gateway, Event Gateway and Mesh)
• This is a high-visibility, transformative leadership role
• Beyond overseeing our distributed global SDET organization, you will be the primary architect of Kong’s next-generation AI-driven testing strategy
• You will spearhead the transformation of our quality engineering function from traditional automation to an intelligent, agentic, self-healing test substrate, using LLMs and automated prompt/agent loops to test the very infrastructure powering the AI era
• Global Team Leadership & Operations: Lead, mentor, and grow a high-performing, geographically distributed team of Software Development Engineers in Test (SDETs) across North America, Europe, and Asia
• Architect the AI Testing Strategy: Define and execute the roadmap for integrating AI, LLMs, and autonomous test agents directly into our testing lifecycle. Implement generative AI capabilities for automated test case generation, smart failure triage, self-healing test scripts, and dynamic edge-case synthesis
• Own Runtimes Quality Strategy: Establish testing strategies for low-latency, high-throughput C/Lua/Rust data planes
• Modernize Frameworks & CI/CD Pipelines: Oversee the architectural evolution of our end-to-end, performance, regression, and chaos-testing automation harnesses embedded within modern CI/CD pipelines (GitHub Actions, Docker, K8s)
• Shift-Left Quality & Governance: Drive quality ownership upstream. Partner closely with Software Engineering and Product Management to embed rigorous testability standards into initial architectural designs
• Define Enterprise-Grade Metrics: Establish and monitor clear QE health indicators (e.g., CI signal reliability, automation coverage, defect density, time-to-triage, and release stability)