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Extreme Networks is seeking an AI Staff Software Systems Engineer to help shape the future of intelligent networking, generative AI, and autonomous agentic systems. You will work at the forefront of intelligent networking, agentic automation, cloud-native platforms, and real-time distributed systems, helping lead the development of core platforms and services that process large volumes of network and operational data and power AI experiences across the company.
You will serve as a thought leader and forward thinker, helping define and drive an 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. Your responsibilities include leading the end-to-end software development lifecycle (architecture, design, implementation, testing, deployment, operations), designing and building high-performance production-ready services for next-generation real-time data and AI platforms, and mentoring engineers across the team to establish clear technical direction and foster a culture of innovation and 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. The role involves leading technical discussions and strategy while participating hands-on in design reviews, code reviews, and implementation.
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, creating tremendous opportunity and growth across regions. The group you'll join is responsible for developing core platforms and services spanning networking, agentic AI, distributed data processing, real-time analytics, and intelligent automation that impact multiple flagship products and new company 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
