AI Infrastructure Engineer

Join to apply for the

AI Infrastructure Engineer

role at

YTL AI Labs

At YTL AI Labs, we build sovereign AI models that perform on par with the world’s best—while staying grounded in local needs, values, and context. Our flagship model, Ilmu, is designed to be culturally aware, contextually intelligent, and fluent in Bahasa Melayu, delivering cutting-edge solutions that empower Malaysian businesses with intelligence that truly understands the market and the people they serve.

As pioneers of sovereign AI, we believe every nation should have the power to shape its own intelligence—guided by its people, priorities, and principles.

About the Role As an

AI Infrastructure Engineer , you’ll build and maintain the systems that power our AI applications and model training. Your work will ensure our teams can run AI workloads reliably, securely, and at scale — across both cloud and on-prem environments.

This role combines DevOps, ML systems, and infrastructure engineering to support everything from LLMs to training pipelines.

What you’ll do

Build and maintain infrastructure for model hosting, prompt execution, and training workflows

Operate and optimize model-serving systems like vLLM, Triton, or OpenAI proxies

Implement secure API gateways and manage token usage, routing, and fallback

Develop and support training pipelines, GPU scheduling, and experiment tracking

Maintain CI / CD systems, observability tools, and infrastructure documentation

What We’re Looking For

6+ years' experience in infrastructure engineering, DevOps, or ML systems

Strong command of Kubernetes, Terraform, and cloud-native architecture (AWS, Azure, GCP)

Experience with containerization, CI / CD, and API security practices

Prior exposure to model hosting or ML pipeline orchestration

Understanding of networking concepts including VPNs, VNets, and hybrid connectivity

Familiarity with security best practices for cross-platform infrastructure

Experience with on-prem infrastructure including networking, storage hardware

Bonus points if you have

Experience with GPU resource orchestration or Kubeflow

Familiarity with inference servers like vLLM, Triton, TGI, or TorchServe

Understanding of cost telemetry and resource budgeting for model traffic

Security mindset and experience with IAM, logging, and compliance

Familiarity with compliance frameworks (SOC2, GDPR, HIPAA) and implementing controls

Background in database management across different platforms

If you’re looking to do meaningful work with people who care about how we get there — we’d love to meet you. Apply now!

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