Lead/ Staff Backend Engineer – Communications AI Infrastructure at RingCentral

Bengaluru, Karnataka, India

Not SpecifiedCompensation

Senior (5 to 8 years), Expert & Leadership (9+ years)Experience Level

Full TimeJob Type

UnknownVisa

Technology, AI, CommunicationsIndustries

10–14 years of hands-on backend engineering building and operating large-scale distributed systems, with a deep understanding of consistency, partitioning, replication and high availability fundamentals
Strong system design expertise, with the ability to architect fault-tolerant, horizontally scalable services
Expert proficiency in at least one modern backend language (e.g., Java, Kotlin, Python, Go), with the ability to write high-performance code and lead technical reviews across complex systems
Experience designing and scaling high-throughput or low-latency services, including API design, concurrency models, resource management and performance tuning across distributed workloads
Proven ability to build event-driven or asynchronous architectures using distributed messaging or streaming systems (e.g., Kafka, Kinesis, Pulsar, RabbitMQ)
Hands-on performance and observability skills, including profiling, eliminating bottlenecks, optimizing tail latency and implementing robust metrics, tracing, and logging for distributed systems
Nice to Have: Understanding of networking fundamentals and cross-region or multi-DC architectures, including latency tradeoffs and data movement considerations

Build ultra-reliable, low-latency backends for real-time calls, messaging, and media workflows at scale
Design high-throughput event pipelines that power real-time signaling, analytics and AI feature processing with minimal lag
Design distributed coordination and routing systems that maintain consistency and performance across regions
Develop scalable platform primitives like RESTful APIs, identity, sessioning, rate limits and storage that serve as foundations for AI-powered applications
Create end-to-end observability that keep a multi-region UCaaS platform predictable under heavy load