Overview
AI Staff SW Systems Engineer focused on AI infrastructure, distributed systems, microservices, and agentic AI for intelligent networking and real-time data.
What you'll do
- Define and drive an innovative technical vision across products and platforms.
- Design and launch capabilities across intelligent networking, distributed systems, real-time processing, agentic AI, and machine learning.
- Lead end-to-end software development lifecycle from architecture through operations.
- Design and build high-performance, production-ready services including scalable data pipelines and distributed microservices.
- Lead technical discussions with hands-on participation in design and code reviews.
- Mentor and develop engineers and establish technical direction for the team.
- Build resilient, secure, observable, scalable systems and champion engineering/operational improvements.
What you'll need
- 7+ years across the complete software development lifecycle including architecture, testing, deployment, and production operations.
- 7+ years of programming experience in at least one general-purpose language (preferably Python, Java, Go, or C++).
- 3+ years leading design and architecture of large-scale distributed systems, preferably on AWS/Azure/Google Cloud.
- Experience in areas including real-time microservices, stream processing, distributed data platforms, cloud infrastructure, AI/agentic systems, network telemetry, or large-scale analytics.
- Ability to design systems processing high-volume, high-velocity data with stringent requirements for scalability, availability, latency, and reliability.
- Experience mentoring engineers and serving as a technical lead or leading an engineering team.
- Bachelor’s degree in Computer Science/Engineering/Mathematics (or equivalent practical experience).
Nice to have
- Master’s or PhD in a related discipline (or equivalent practical experience).
- Experience building event-driven, highly scalable microservices and processing real-time operational/network data.
- Experience building generative AI/agentic systems including tool use, planning, memory, retrieval, orchestration, evaluation, and production deployment.
- Experience with networking, network management, observability/telemetry, cloud infrastructure, or distributed control-plane systems.
- Experience with distributed data/processing technologies such as Kafka, Spark, Flink, PySpark, MapReduce, or comparable technologies.
