AI Platform Engineer

Systems Limited - APAC; Kuala Lumpur; On-site; MYR 180,000 - 300,000; Full time

Job summary

Systems Limited - APAC seeks an AI Platform Engineer with 6–12+ years of experience to build and operate the shared AI platform infrastructure that underpins AI deployments across practices.

You will own internal tooling, standardize CI/CD pipelines for AI workloads, and drive platform cost governance and security posture in partnership with AI Security Engineers.

Qualifications

• 6–12+ yrs platform/infrastructure engineering, with 2+ yrs supporting AI/ML workloads specifically.
• Deep cloud infrastructure expertise (IaC, Kubernetes, networking, IAM), including hosting vector/graph databases.
• Experience building internal developer platforms/tooling, not just running infrastructure.
• Experience integrating and operating managed AI/agentic platforms alongside self-hosted stacks.
• Familiarity with multi-tenant capacity planning and cost allocation.
• Experience with platform-level security hardening.
• Cross-practice stakeholder management and ability to balance infra requests.
• Cost/capacity planning literacy for leadership communications.
• Documentation of platform capabilities to enable self-serve.

Responsibilities

• Build and maintain shared AI platform infrastructure — compute provisioning, networking, IAM for AI workloads.
• Own internal tooling and templates used to deploy models/agents consistently.
• Standardize CI/CD pipelines for AI workloads across practices, including shared evaluation platforms.
• Manage platform-level cost governance and capacity planning across concurrent engagements.
• Own platform security posture in partnership with AI Security Engineers.
• Partner with MLOps/LLMOps Engineers on the boundary between platform and workload-specific operations.
• Balance competing infrastructure requests from multiple practice leads.
• Document platform capabilities clearly enough that practices can self-serve.
• Forecast and justify platform spend to non-technical leadership.

Skills

Cloud infrastructure

IaC

Kubernetes

Security hardening

Cost governance

MLOps collaboration

Platform tooling

Multi-tenant planning

Tools

Azure AI Foundry

AWS Bedrock

Google Vertex AI

Open-source stacks

Job description

We are seeking a AI Platform Engineer with approximately 6–12+ years of experience in the field. Builds and operates the shared AI platform infrastructure — the paved road every AI practice builds on top of, so no team reinvents deployment plumbing.

Responsibilities:

• Build and maintain shared AI platform infrastructure — compute provisioning, networking, IAM for AI workloads
• Own the internal tooling and templates practices use to deploy models/agents consistently
• Standardize CI/CD pipelines for AI workloads across practices, including shared AI evaluation platforms
• Manage platform-level cost governance and capacity planning across concurrent engagements
• Own platform security posture in partnership with AI Security Engineers
• Partner with MLOps/LLMOps Engineers on the boundary between platform and workload-specific operations
• Balance competing infrastructure requests from multiple practice leads
• Document platform capabilities clearly enough that practices can self-serve
• Forecast and justify platform spend to non-technical leadership

Requirements:

• 6–12+ yrs platform/infrastructure engineering, with 2+ yrs supporting AI/ML workloads specifically
• Deep cloud infrastructure expertise (IaC, Kubernetes, networking, IAM), including hosting vector/graph databases
• Experience building internal developer platforms/tooling, not just running infrastructure
• Experience integrating and operating managed AI/agentic platforms — Microsoft Azure AI Foundry, AWS Bedrock, and Google Vertex AI — alongside self-hosted open-source stacks as a good-to-have
• Familiarity with multi-tenant capacity planning and cost allocation
• Experience with platform-level security hardening
• Cross-practice stakeholder management — balances competing infra requests from multiple practice leads
• Cost/capacity planning literacy — can forecast and justify platform spend to non-technical leadership
• Documents platform capabilities clearly enough that practices can self-serve
• Collaborative — builds shared infrastructure without becoming a bottleneck
• Success metrics: platform uptime/reliability · cost per workload vs. budget · practice self-service adoption rate