We are building the backbone for the next generation of human communication. Imagine a system that doesn't just transmit voice and video, but understands it in real-time, transcribing, summarizing and retrieving context across millions of concurrent sessions.

As a Staff Engineer, you aren't just "building features"; you are the architect of the high-concurrency data and inference runtime that makes real-time AI possible. You will be building core components of the distributed platform and design to solve low-latency RAG on live streaming data

Join our Bengaluru engineering team to reimagine how people communicate using AI.

What You’ll Do

Architect Core Infrastructure: Design ultra-reliable, low-latency backends and high-throughput event pipelines for real-time signaling and AI feature processing
Scale Distributed Systems: Build robust data movement: streaming jobs, backfills, reprocessing and CDC with guardrails (schema/versioning, replay safety, idempotency)
Develop Platform Primitives: Create scalable foundations including RESTful/gRPC APIs, data/feature pipelines, retrieval interfaces, identity systems, sessioning and rate-limiting
Drive Technical Excellence: Profile hot paths to reduce p99 latencies, optimize indexing throughput, and lead technical reviews across complex, multi-service systems.
Advance AI Retrieval & RAG: Productionize indexing, chunking, and re-ranking flows in OpenSearch; drive quality through disciplined offline/online evaluation and experiments.
Leadership through Competence: You are a force multiplier. You mentor senior engineers by consistently raising the bar on design docs and system reliability.

What You Bring

Engineering Depth: 10–14 years of experience (or equivalent proven impact) with a track record of building large-scale distributed systems with deep knowledge of partitioning, replication and high availability.
Language Expertise: Expert proficiency in Java, Kotlin; ability to write high-performance code, optimise garbage collection and reduce latencies
System Design Instincts: Proven ability to design fault-tolerant, horizontally scalable services and manage complex concurrency models and focus on SLA/SLO metrics.
Search & Data Streaming: Hands-on experience with OpenSearch/Elasticsearch (indexing strategies/query tuning) and streaming frameworks like Apache Flink, Kafka, or Pulsar.
RAG Proficiency: Experience building and improving RAG-style features in production (embeddings, retrieval, and structured experimentation.

Nice to Have

Understanding cross-region or multi-DC architectures, including latency tradeoffs and data movement considerations

Our Tech Stack

Languages: Java (Primary), Kotlin, Go, or Python.
Data/Search: OpenSearch, Apache Flink, Kafka, SQL/NoSQL and Redis.
Infrastructure: Kubernetes, AWS, gRPC/HTTP, Service Mesh.
Observability: Robust metrics, tracing, and logging for distributed workloads.