Kubernetes Networking Platform Senior Engineer

The Kubernetes Networking Platform Senior Engineer will lead the design, delivery, and operation of networking capabilities across the enterprise Kubernetes platform. This includes critical components such as ingress controllers, service mesh, DNS, and traffic management. This engineer will join a team responsible for building a secure, scalable, and observable networking layer that enables application teams to seamlessly connect, communicate, and expose services within and outside the cluster. The ideal candidate brings deep experience in distributed systems and networking, and is passionate about building platform abstractions that simplify complexity for developers while maintaining enterprise-grade reliability and security. We are transforming the way technology is managed. Automation, DevOps, and product-oriented platform engineering are the new standards as we enable rapid innovation, speed-to-market, and resilient operations. As we are early in this journey, strong technical leadership and the ability to influence and elevate others is critical.

Core Work Activities: Kubernetes & Platform Engineering:

• Design, build, and operate Kubernetes networking capabilities including ingress, service mesh, and DNS
• Develop and maintain standardized, self-service networking patterns for application teams
• Implement and manage traffic routing strategies, including canary deployments, blue/green releases, and failover mechanisms
• Ensure secure communication through network policies, mTLS, and zero-trust principles
• Continuously improve platform reliability, scalability, and performance through automation and observability
• Troubleshoot complex networking issues across distributed systems and drive root cause analysis
• Partner with security, platform, and application teams to define and enforce networking standards
• Build tooling and automation to improve developer experience and reduce operational overhead
• Maintain clear and consumable documentation for platform users
• Stay current with emerging trends in Kubernetes and cloud-native networking

• 6+ years of technology experience, including:
• 3+ years in a platform, infrastructure, or systems engineering role
• 3+ years working with public cloud platforms (AWS, Azure, Google Cloud Platform)
• Strong experience with Kubernetes, including:
• Networking fundamentals (CNI, service discovery, load balancing)
• Kubernetes networking primitives (Services, Ingress, NetworkPolicy)
• Hands-on experience with Kubernetes networking components, such as:
• Gateway and Ingress controllers (e.g., kgateway, NGINX, ALB, or similar)
• Service mesh technologies (e.g., Istio, Cilium, or similar)
• DNS systems (CoreDNS, External DNS, or enterprise DNS integration)
• Experience designing and operating highly available, distributed systems (99.99% uptime) with attention to latency, resiliency, and failure modes
• Strong troubleshooting skills across layers (application, network, infrastructure)
• Proven ability to implement Infrastructure as Code and automation using tools such as Terraform, Helm, and GitOps workflows
• Mindset of automate first , continuously identifying and eliminating manual processes
• Experience working within a platform-as-a-product model, including:
• Treating internal platform capabilities as products
• Gathering feedback from users (application teams)
• Iterating based on adoption and usability
• Strong collaboration habits, including:
• Code reviews as a primary mechanism for quality and knowledge sharing
• Writing clear, user-focused documentation
• Contributing to and evolving engineering standards across teams
• Comfort using AI-powered development tools (e.g., coding assistants, copilots, or similar) to accelerate development, troubleshooting, and documentation
• Ability to critically evaluate AI-generated output, ensuring correctness, security, and alignment with platform standards
• Experience leveraging AI tools to:
• Accelerate Infrastructure as Code development
• Troubleshoot complex system and networking issues
• Improve documentation and developer experience
• Strong engineering judgment to determine when to rely on AI vs. when to deep dive manually, especially in complex distributed systems and production incidents