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Senior Voice Infrastructure Engineer at VC-backed AI infrastructure startup

Join a high-growth AI infrastructure startup as a Senior Voice Infrastructure Engineer and take command of a mission-critical GPU fleet where you will architect and scale low-latency voice pipelines, optimizing model serving for real-time speech and language processing. If you are an expert in Kubernetes and GPU orchestration looking to lead core architectural decisions at an early-stage company, this is a unique opportunity to build technology that impacts millions of users in emerging markets while working directly with founding leadership in a high-impact, London-based environment.

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Location

London, United Kingdom

Compensation

Not Disclosed

Company

Confidential company

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Role overview

You will architect and scale a low-latency voice AI pipeline, orchestrating GPU fleets for real-time inference. Your work ensures sub-second responses for speech-to-text and text-to-speech models while driving down operational costs. This is a high-ownership role building the core infrastructure for a platform serving millions of users in emerging markets.

About the company

VC-backed AI infrastructure startup

What you will do

  • Orchestrate and serve STT, LLM, and TTS models on GPU fleets under strict latency budgets.
  • Manage Kubernetes-based infrastructure and cloud resources to optimize for performance and cost.
  • Lead architectural decisions for a multi-tenant platform handling secure voice data and PII.

Who this is a fit for

  • 5+ years of experience running production-grade infrastructure with a focus on GPU inference.
  • Deep expertise in Kubernetes, AWS, and inference engines like vLLM or Triton.
  • Proven track record of owning complex systems end-to-end in high-growth startup environments.

Why this role is remarkable

  • Lead mission-critical GPU infrastructure projects powering real-time voice agents at global scale.
  • Join an early-stage team backed by top-tier VCs in the high-growth AI sector.
  • Influence major architectural decisions and cost-efficiency metrics for a production-grade platform.

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Jill
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