AI Platform Engineer – Secure Inference & GPU Infra

Leonardo

Basildon

Hybrid

GBP 90,000 - 135,000

Full time

6 days ago
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Benefits offered by this job

Pension scheme (up to 15% employer)
Flexible hours
Hybrid working
Coursera/LinkedIn Learning access
Referral bonus

Job summary

Leonardo is recruiting for AI Platform Engineers to shape, build and operate the infrastructure that underpins our AI platform. You will enable secure, reliable access to AI models and inference services, while ensuring performance and security across the enterprise.

This is a hybrid role with multiple locations in the UK, including Edinburgh. We value practical experience with GPU-based platforms, containers, and modern observability practices.

Qualifications

  • Experience operating shared or business-critical platforms.
  • Familiarity with large language models and practical considerations of running them.
  • Experience troubleshooting distributed systems and performance issues.
  • Familiarity with infrastructure as code and CI/CD.
  • Effective communication and stakeholder engagement.
  • Curiosity and a drive for continuous improvement.

Responsibilities

  • Take ownership of the platform layer above our managed GPU infrastructure, including model serving and inference runtimes.
  • Deploy and operate scalable inference services, improve observability and performance, and investigate complex issues across the platform.

Skills

Platform ownership
LLM familiarity
Distributed systems
Infra as code
CI/CD
Stakeholder engagement
Curiosity

Tools

Docker
Kubernetes
vLLM
AI gateways
LiteLLM
Python

Job description

Leonardo is recruiting for AI Platform Engineers to shape, build and operate the infrastructure that underpins our AI platform. You will enable secure, reliable access to AI models and inference services, while ensuring performance and security across the enterprise.

This is a hybrid role with multiple locations in the UK, including Edinburgh. We value practical experience with GPU-based platforms, containers, and modern observability practices.

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