What youll be doing:
Lead and mentor a team of infrastructure and tooling engineers; set technical direction, define priorities, and grow team capabilities.
Design, build, and maintain scalable infrastructure for development, integration, and test environments supporting SONiC OS.
Architect and deliver LLM-based tools for intelligent regression analysis – failure classification, root cause clustering, anomaly detection, and test flakiness prediction.
Lead efforts to reduce regression runtime through parallelization, smart test selection, and dependency-aware scheduling.
Develop deep technical knowledge of SONiC Network OS internals, including its subsystem architecture, SAI/ASIC abstraction layer, and management plane.
What we need to see:
B.Sc. degree or equivalent experience in Engineering/Computer Science/related field.
8+ overall years of software engineering experience, with at least 3 years of experience in a leadership role, managing software development teams.
Proven ability to lead technical teams: hiring, mentoring, technical roadmapping, and cross-team influence.
Experienced with developing software testing tools and tests infrastructure.
Strong Python programming skills; experience building production-quality automation frameworks and tooling.
Demonstrated experience designing and operating CI/CD systems at scale (Jenkins, GitLab CI, GitHub Actions, or equivalent).
Hands-on experience with LLMs or AI-assisted developer tooling – building, integrating, or productizing AI capabilities in an engineering workflow.
Strong analytical and problem-solving skills with a bias toward measurable outcomes and data-driven decisions.
Ways to stand out from the crowd:
Deep Linux expertise: system internals, networking stack, process management, and scripting.
Prior experience building LLM-powered test analysis pipelines or AI-enhanced DevOps tooling in a real production environment.
Knowledge of networking protocols and hardware: Ethernet switching, L2/L3 protocols, QoS, VLANs, high-performance data center networking.
Experience with code coverage instrumentation in large-scale C/Python codebases and using coverage data for test prioritization.
Track record of measurably improving regression runtime, test reliability, or CI throughput in a complex embedded or systems software environment.
