Senior Voice AI Platform Engineer

Aivar Innovations; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Convogent Bangalore Office is seeking an experienced engineer to own the distributed, real-time systems that orchestrate thousands of voice calls. You will ensure fast, reliable, and natural conversations by coordinating STT/ASR, LLM, TTS, RAG, and tool-calling across the full pipeline.

You will optimize latency, measure quality, and balance tradeoffs between components. The role includes mentoring a growing team and extending the platform to live assist capabilities.

Qualifications

• 5+ years building production systems, with 2+ years in real-time voice in production.
• Deep understanding of the full voice pipeline end-to-end and its impact on quality and latency.
• Strong Go and Python, production-grade, performance-critical code; rewrite hot-path components to lift throughput.

Responsibilities

• Own voice AI at scale; keep thousands of concurrent calls fast by extending Pipecat or rewriting hot paths in Go.
• Own end-to-end latency; keep conversations under ~800ms across STT, LLM, TTS, RAG, tool-calling, and network hops.
• Diagnose bad calls quickly by reading signals and fixing the pipeline.
• Make quality measurable with voice-to-voice evals that gate releases and detect provider drift.
• Own provider tradeoffs between quality and latency per conversation flow.
• Build adjacent products and extend platform into live/agent assist on the same foundations.
• Set the bar by reviewing, mentoring, and defining engineering standards for growth.

Skills

Go

Python

Real-time voice

Latency optimization

Distributed systems

Pipecat

Eval-driven QA

Tools

Pipecat

Job description

Platform: Convogent

Level: Mid–Senior (5+ years | 2+ years building voice AI in production)

Location: Bangalore Office

About The Role

You will own the distributed, real-time systems that orchestrate parallel voice calls at massive scale: the core platform behind every conversation Convogent runs. The job is keeping thousands of simultaneous calls fast, reliable, and natural, owning how STT/ASR, LLM, TTS, RAG, and tool-calling work together, and where every millisecond and every point of quality goes across the pipeline.

What You’ll Do

• Own voice AI at scale. Keep the pipeline fast and reliable at enterprise concurrency, thousands of simultaneous calls, by lifting calls-per-vCPU through extending Pipecat or rewriting hot paths in Go.
• Own end-to-end latency. Keep the conversation under ~800ms by decomposing the budget across STT, LLM, TTS, RAG, tool-calling, turn-detection, and network hops.
• Diagnose the bad call. When a call does not feel right, locate the fault in the pipeline fast by asking the right questions, reading the right signals, and owning the fix.
• Make quality measurable. Build voice-to-voice evals that gate releases and catch STT/LLM/TTS provider drift before customers do.
• Own provider tradeoffs. Decide how STT / LLM / TTS / RAG / tool-calling choices balance voice quality against latency, per conversation flow.
• Build adjacent products. Extend the platform into live assist, agent assist, and what comes next, on the same pipeline foundations.
• Set the bar. Review, mentor, and define engineering standards for a growing team.

What We’re Looking For

• 5+ years building production products and systems, with at least 2 of those years in real-time voice, operating it in production, not just shipping a demo.
• Deep understanding of the full voice pipeline, every stage end to end and how each one affects the next and shapes quality and latency.
• Strong in Go/Python, comfortable across the whole pipeline. Production-grade, performance-critical Go is especially valued: you have rewritten hot-path components in Go to lift throughput under real load.
• A track record of running voice AI at scale, scaling Pipecat (or a comparable real-time voice framework) to high concurrency in production.
• Provider-tradeoff fluency: how STT / LLM / TTS / RAG / tool-calling choices play off each other on quality and latency.
• An eval-driven approach to quality. You measure voice quality with real signals, not vibes, and have built or run evals on a real-time voice or LLM system.
• Fluency with agentic coding tools and a habit of treating quality as a measured signal.

What We’re NOT Looking For

• A prompt engineer / \"AI app\" builder who has only called LLM APIs and never operated a real-time pipeline under load.
• Someone who has only used managed platforms (Vapi/Retell/Bland) and never scaled the layer beneath them.
• A pure web-backend CRUD engineer with no real-time/streaming/voice exposure.
• An ML researcher who wants to train/fine-tune models. We integrate providers; we don't build models.