# Local LLM, Voice Agent & AI Infrastructure Lab (AI Infrastructure & Voice Systems)

> **Summary**: A hands-on AI infrastructure lab for private local language models, real-time conversational voice agents, GPU-accelerated speech recognition and reliable workflow automation.
> **Role**: AI infrastructure engineer · Architecture, integration & operations (2026 — ongoing)
> **Tech Stack**: Local LLMs, AI Agents, Whisper, LiveKit, Docker, GPU Inference, Speech Recognition, Voice Agents

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- **Canonical Web Page**: https://shadikur.com/work/local-llm-voice-agent-ai-infrastructure-lab
- **Source Repository**: #private-repo
- **Live / Demo URL**: 

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## The Problem
Practical conversational AI systems must balance privacy, response latency, speech quality, hardware constraints and operational reliability across several tightly coupled services.

## What Was Built & Technical Solution
I designed and operated a containerized lab integrating local large language models, GPU-accelerated Whisper speech recognition, LiveKit real-time communications, voice-agent orchestration and supporting automation. The work includes model and hardware evaluation, service integration, latency tuning, monitoring and deployment workflows.

## Results & Impact
The lab provides a working environment for experimenting with private AI assistants and real-time voice systems while demonstrating end-to-end capability across GPU infrastructure, speech pipelines, container operations, observability and production-minded AI integration.

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## Author & Context
- **Creator**: Mohammad Shadikur Rahman (Senior Infrastructure Architect & Full Stack Engineer)
- **All Projects**: https://shadikur.com/work.txt
- **LLM Index**: https://shadikur.com/llms.txt