Voice AI / Voice Agents Engineer

Company Description ChatBucket is a leading AI-powered multilingual communication platform that enables seamless, real-time conversations across languages. The platform eliminates the need to switch apps or rely on awkward translations, allowing natural dialogue that flows smoothly. Designed for a world where people speak different languages, ChatBucket helps friends, businesses, customers, and global teams communicate without barriers. Using advanced AI, voice, and language technologies, it preserves context, tone, and intent in every interaction to deliver frictionless, authentic conversations.

Role Description This is a full-time, on-site Voice AI / Voice Agents Engineer role based in Hyderabad. The Voice AI / Voice Agents Engineer will design, build, and optimize voice-enabled agents that power multilingual conversations on the ChatBucket platform. Day-to-day responsibilities include developing and integrating speech recognition, text-to-speech, and dialogue management components; collaborating with product and design teams to define voice interaction flows; and implementing features that improve latency, accuracy, and user experience. The role also involves monitoring system performance, troubleshooting issues in production, and iterating on models and pipelines based on user feedback and analytics. The engineer will work closely with cross-functional teams to ensure voice agents scale reliably and support nuanced, context-aware interactions in multiple languages.

Qualifications

Voice AI / Voice Agents Engineer

About the Role

We are looking for a **Voice AI / Voice Agents Engineer** to design, build, scale, and operate production-grade conversational voice systems.

You will work across **speech recognition, LLMs, TTS, real-time audio processing, agent orchestration, distributed systems, and cloud infrastructure** to build voice agents that can understand users, reason over context, take actions, and respond naturally with low latency.

The role requires both strong AI/ML expertise and the ability to take voice-agent systems from prototype to **high-concurrency, production-scale deployments**.

Responsibilities

Voice AI & Agent Engineering

* Design and develop end-to-end voice-agent architectures integrating **ASR, LLMs, TTS, VAD, and agent orchestration**.

* Build real-time streaming voice interactions with low latency and natural turn-taking.

* Develop agent capabilities including tool calling, function execution, memory, context management, and multi-step reasoning.

* Integrate voice agents with APIs, databases, CRMs, telephony platforms, and enterprise systems.

* Work with multilingual, noisy, and code-switching speech environments.

* Evaluate and improve ASR accuracy, TTS quality, conversational quality, interruption handling, and agent reliability.

Scaling & Distributed Systems

* Design systems capable of supporting **high volumes of concurrent voice sessions**.

* Build horizontally scalable services for real-time audio streaming, inference, agent orchestration, and session management.

* Design architectures for **thousands to potentially millions of conversations**, depending on product requirements.

* Optimize GPU and CPU utilization for large-scale model inference.

* Implement load balancing, autoscaling, connection management, queuing, caching, and backpressure mechanisms.

* Design systems that maintain predictable latency as concurrency increases.

* Optimize **GPU memory, batching, continuous batching, model serving, and inference throughput** where applicable.

* Build fault-tolerant systems with graceful degradation and recovery from model, network, service, and provider failures.

* Design multi-region or highly available architectures when required.

* Optimize infrastructure and model usage for **cost per conversation/minute** at scale.

* Establish capacity planning and performance benchmarks for concurrent sessions.

* Identify and eliminate bottlenecks across the entire voice pipeline, from audio ingestion to model inference and response generation.

Production Engineering & Observability

* Deploy and operate AI services in production using cloud infrastructure and containerized environments.

* Build monitoring and observability for latency, throughput, concurrency, errors, model performance, GPU utilization, and infrastructure health.

* Establish SLIs/SLOs for voice-agent systems…