Description
The Cisco cnBNG Control Plane team builds Cisco's cloud-native Broadband Network Gateway - the carrier-grade subscriber-management brain that runs as microservices on the SMI (Subscriber Microservices Infrastructure) / Kubernetes platform and interworks with IOS-XR, Silicon One-based User Plane Functions (UPFs).
As a Software Engineer on the cnBNG Control Plane, you will own the design, delivery, and end-to-end quality of carrier-grade subscriber-management features in a cloud-native architecture.
Responsibilities:
- Design, develop, and maintain cnBNG Control Plane microservices (C/C++/Go) on the SMI/Kubernetes platform, interworking with IOS-XR / Silicon One-based User Plane Functions.
- Implement and enhance subscriber-management functionality: PPPoE/IPoE session bring-up, DHCPv4/DHCPv6 (server/proxy/relay), Prefix Delegation, RADIUS/AAA, PAP/CHAP, accounting, Change of Authorization, IPAM, subscriber policy, and QoS.
- Develop and harden CP-UP separation over the State Control Interface - session programming, state download/replay, and reconciliation between the Control Plane and Silicon One UPFs.
- Build features for scale and high availability: cluster upscale, N:M and geo/warm redundancy (SRG), CP-GR switchover, and subscriber-session continuity during CP-UP communication failures.
- Own quality end-to-end: write unit tests and component/integration tests; design and execute system, regression, scale, performance, resiliency, and high-availability tests for your features.
- Develop reusable, scalable test automation (Python; pyATS/Genie or equivalent) and integrate it into CI/CD pipelines and regression suites.
- Build and maintain containerized/virtual/cloud-based test environments (Docker, Kubernetes, SMI) and validate subscriber-scale, traffic, failover, upgrade/rollback, process-restart, and subscriber-churn scenarios.
- Optimize control-plane performance, memory, and convergence for millions of subscribers across large UPF fleets.
- Develop and maintain gRPC/REST APIs and YANG/OpenConfig models, plus CLI and model-driven telemetry for configuration and operational visibility.
- Troubleshoot sophisticated, distributed issues across CP, Silicon One UPFs, and the SMI/Kubernetes stack; drive RCA using logs, traces, core dumps, RADIUS transactions, and telemetry.
- Manage code with Git; participate in code reviews; collaborate with Test Engineering on feature, scale, and HA validation.
- Work with architects, DE/DT leads, and PLM to deliver features across the release lifecycle (BC/CC, AC/EC, DEVELOPING, FCS).
- Actively use approved AI coding assistants and engineering agents for code generation, unit/automation test creation, static analysis, log analysis, defect triage, and documentation - validating AI-generated output responsibly.
Minimum Qualifications:
- Bachelor's or master's degree in Computer Science, Electrical/Computer Engineering, Telecommunications, or a related field.
- Experience in systems/embedded or cloud-native software engineering for networking products.
- Strong programming in C/C++ and/or Go on Linux; Python for test automation and scripting.
- Experience writing unit, integration, system, and scale/performance tests, and building automation (pyATS/Genie or equivalent) integrated into CI/CD and regression pipelines.
- Solid understanding of IP networking and subscriber/broadband protocols: PPPoE, IPoE, DHCPv4/v6, RADIUS/AAA, PPP, L2TP.
- Knowledge of Cisco Silicon One (SiOne) NPU-based User Plane Functions (UPFs) - data-plane/forwarding architecture, CP-to-UPF programming, and how subscriber sessions map to NPU forwarding.
- Cloud-native fundamentals: Docker, Kubernetes, microservices, and service meshes.
- Experience building and consuming gRPC/REST APIs and data models (YANG, protobuf).
- Distributed-systems concepts: high availability, redundancy, state reconciliation, and scale.
- Strong Linux skills; experience with Git, code reviews, debugging, and CI/CD pipelines.
- AI-assisted engineering is a must: use of AI coding assistants (e.g., GitHub Copilot) and a working understanding of generative AI, AI agents, MCP (Model Context Protocol), reusable AI skills, prompt/context management, and responsible validation of AI-generated output.
Benefits:
- Opportunities for growth and professional development
- Collaborative and dynamic work environment
- Flexible work arrangements
- Access to cutting-edge technologies and tools