AI Platform Engineer - Hybrid Inference & GPU Infra

Leonardo; Basildon; Hybrid; GBP 80,000 - 120,000; Full time

Benefits offered by this job

Time to Recharge

Pension scheme

Mental health support

Bonus scheme

Learning access via Coursera/LinkedIn

Refer a friend

Flexible benefits up to £500

Hybrid working

Job summary

Leonardo is recruiting AI Platform Engineers to shape, build and operate the infrastructure that underpins our AI platform. You’ll enable secure, reliable access to AI models and inference services while maintaining security, performance and availability.

The role focuses on deploying scalable inference services with containers and Kubernetes, setting standards for operating AI services in a secure enterprise, and improving observability within a hybrid, multi-location environment.

Qualifications

• Experience operating shared or business-critical platforms.
• Familiarity with large language models and the 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, inference runtimes, AI gateways and supporting services.
• Deploy and operate scalable inference services using containers and Kubernetes, improve observability and performance, and investigate complex issues.

Skills

Platform experience

LLMs familiarity

Distributed systems

Infrastructure as code

CI/CD

Stakeholder engagement

Curiosity & improvement

Tools

Docker

Kubernetes

vLLM

SGLang

LiteLLM

Bifrost

Job description

Leonardo is recruiting AI Platform Engineers to shape, build and operate the infrastructure that underpins our AI platform. You’ll enable secure, reliable access to AI models and inference services while maintaining security, performance and availability.

The role focuses on deploying scalable inference services with containers and Kubernetes, setting standards for operating AI services in a secure enterprise, and improving observability within a hybrid, multi-location environment.