Build and maintain scalable, low-latency backend systems for real-time AI and voice workflows. Responsibilities include developing microservices, APIs, streaming systems, concurrent and fault-tolerant code, observability, testing, deployment, and monitoring. The role integrates WebRTC and telephony protocols, Voice AI, LLMs, vector databases, and agent frameworks while collaborating with product, AI, and ML teams on multi-agent orchestration and automation.
SDE II, Backend — Voice AI & Telephony
Experience: 2–4 Years
Location: Bengaluru (Work from Office)
Team: Blue Machines
Role Overview
We’re looking for an SDE II (Backend) to help build high-scale, low-latency systems for our AI and voice platform. You’ll work on real-time backend systems that power live voice conversations — combining strong backend fundamentals with exposure to WebRTC, SIP-based telephony, and Voice AI.
Responsibilities
- Build and maintain scalable microservices and backend systems that power real-time AI and voice workflows.
- Contribute to system design — APIs, data models, deployment, and monitoring — alongside senior engineers.
- Write performant, concurrent, fault-tolerant code at scale.
- Implement observability and testing best practices (Datadog, Sentry, etc.).
- Collaborate with product and AI teams on the platform’s multi-agent orchestration and automation layer.
- Partner closely with the ML team to integrate in-house models, support benchmarking and observability, and help build the next generation of in-house AI models.
Requirements
Must-Have Skills- 2–4 years of backend development experience in Python or Java.
- Strong grasp of system design, concurrency, and distributed systems.
- Experience building scalable APIs or streaming systems.
- Familiarity with WebRTC, SIP, RTP, or other real-time communication protocols.
- Experience working with Voice AI or speech processing systems (ASR/TTS, real-time inference).
- Experience integrating LLMs, vector DBs, or AI agent frameworks into production systems.
- Experience with cloud environments (GCP/AWS) and containerized deployments (Kubernetes, Docker).
- Sound understanding of databases (MongoDB, Redis, Postgres) and message queues (Kafka, Pub/Sub).
- Collaborate with product and AI teams to evolve the platform’s multi-agent orchestration and automation layer.
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