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Extreme Networks is seeking an AI Staff Software Systems Engineer to help shape the future of intelligent networking and autonomous agentic systems. You will work at the forefront of AI infrastructure, distributed systems, and real-time data processing, leading the development of core platforms and services that power intelligent experiences across the company.
In this role, you will serve as a thought leader and forward thinker, helping define and drive innovative technical vision across products and platforms. You will design and launch strategic capabilities spanning intelligent networking, distributed systems, real-time data processing, agentic AI, and machine learning. You will lead the end-to-end software development lifecycle, including architecture, design, implementation, testing, deployment, and operations, while participating hands-on in design reviews, code reviews, and implementation.
You will design and build high-performance, production-ready services for next-generation real-time data and AI platforms, including scalable data pipelines, distributed microservices, agentic solutions, and integrations with networking systems. A key responsibility is mentoring and developing engineers across the team, establishing clear technical direction, and fostering a culture of innovation, collaboration, accountability, and engineering excellence.
You will uphold the highest standards of technical rigor and operational excellence, building highly resilient, secure, observable, and scalable systems. You will champion improvements to architecture, engineering processes, system performance, and operational readiness.
Extreme Networks is a global networking leader with over 50,000 customers worldwide, recognized as a Technology Leader in the Gartner Magic Quadrant. The company is experiencing double-digit growth year over year and has recently expanded through acquisitions. You will be part of a group responsible for developing core platforms spanning networking, agentic AI, distributed data processing, real-time analytics, and intelligent automation that impact both flagship products and new offerings.
REQUIREMENTS:
- Bachelor's degree in Computer Science, Engineering, Mathematics, or related technical discipline (or equivalent practical experience)
- 7+ years of experience across the complete software development lifecycle (architecture, design, coding, code reviews, testing, build processes, deployment, production operations)
- 7+ years of programming experience with proficiency in at least one general-purpose language (Python, Java, Go, or C++ preferred)
- 3+ years of experience leading the design and architecture of large-scale distributed systems, preferably on cloud platforms (AWS, Azure, Google Cloud)
- Experience in one or more of: real-time microservices, stream processing, distributed data platforms, cloud infrastructure, AI/agentic systems, network telemetry, or large-scale analytics
- Experience designing systems that process high-volume, high-velocity data with stringent requirements for scalability, availability, latency, and operational reliability
- Experience mentoring engineers, serving as technical lead, or leading an engineering team
- Demonstrated ability to tackle highly complex, ambiguous, or undefined technical problems and translate them into pragmatic, production-ready solutions
PREFERRED QUALIFICATIONS:
- Master's or PhD in Computer Science, Computer Engineering, Electrical Engineering, Machine Learning, or related discipline
- Experience building highly scalable, event-driven microservices and processing large volumes of real-time operational or network data
- Experience building generative AI or agentic systems (tool use, planning, memory, retrieval, orchestration, evaluation, production deployment)
- Experience developing networking, network management, observability, telemetry, cloud infrastructure, or distributed control-plane systems
- Experience with distributed data and processing technologies (Kafka, Spark, Flink, PySpark, MapReduce, or comparable)
- Experience working with large-scale, real-world datasets and building reliable production systems that translate data into actionable insights or automated outcomes
