Cloud Engineer (AI & Voice Technologies)

Overview

About the Role
We are developing a scalable AI-driven Voicebot platform that handles real-time audio interactions. We are seeking an experienced Cloud Engineer to build and maintain a secure, reliable, and production-ready cloud infrastructure capable of supporting low-latency AI workloads and streaming pipelines.

Key Responsibilities

Architect and maintain scalable cloud infrastructure for AI and voice-based applications.

Manage containerized deployments with Docker and Kubernetes.

Implement CI/CD pipelines for automated, reliable deployments.

Set up monitoring and observability stacks for distributed systems.

Ensure strong cloud security practices across networking, IAM, and secrets management.

Optimize infrastructure for cost efficiency, reliability, and performance.

Support real-time WebSocket-based communication flows.

Enable smooth processing of streaming audio chunks and ensure efficient real-time data handling/storage.

Collaborate closely with AI, backend, and voice teams to ensure seamless integration and reliable production workflows.

Troubleshoot deployment, networking, and performance issues in production.

Required Skills & Experience

Strong hands-on experience with AWS or similar cloud providers.

Proficiency with Kubernetes, Docker, and containerized microservices.

Experience with Infrastructure-as-Code tools such as Terraform or CloudFormation.

Solid foundation in Linux systems, cloud networking, and security best practices.

Experience building real-time systems using WebSockets or similar bidirectional communication protocols.

Understanding of audio streaming concepts such as chunked audio transfer, buffering, and low-latency processing.

Ability to integrate real-time audio handling with backend storage or processing pipelines.

Experience working with monitoring/logging tools such as Prometheus, Grafana, ELK Stack, or CloudWatch.

Strong debugging skills for distributed and high-traffic environments.

Preferred / Added Advantage

Experience with LLMOps or ML infrastructure.

Familiarity with popular LLMOps tools such as:

MLflow

Weights & Biases

LangSmith

BentoML

ClearML

Hugging Face Inference or Text Generation Inference

Knowledge of GPU workloads and modern model-serving frameworks.

Experience with audio processing pipelines (e.g., Whisper, ffmpeg, WebRTC, real-time streaming servers).

Exposure to event-driven architectures or messaging systems for high-throughput data flows.

What We Offer

Opportunity to contribute to next-generation AI and voice automation products.

Ownership of a scalable and mission-critical cloud infrastructure.

A fast-paced environment focused on engineering excellence and innovation.

Competitive compensation and significant long-term growth opportunities.