AI Platform Engineer

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

Job summary

Systems Limited is seeking an AI Platform Engineer with 6–12+ years of experience to build and operate a shared AI platform infrastructure in Kuala Lumpur, Malaysia. You will own internal tooling, standardize CI/CD for AI workloads, and manage platform costs and security posture across multiple engagements.

The role requires cross-practice collaboration, multi-tenant capacity planning, and strong documentation skills to enable self-serve capabilities for multiple AI teams.

Qualifications

• 6–12+ years in platform/infrastructure engineering
• 2+ years supporting AI/ML workloads
• Experience building internal developer platforms/tooling
• Experience with managed AI/agentic platforms (Azure AI Foundry, AWS Bedrock, Google Vertex AI)
• Familiar with multi-tenant capacity planning and cost allocation
• Experience with platform-level security hardening
• Cross-practice stakeholder management
• Cost/capacity planning literacy
• Documents platform capabilities clearly for self-serve

Responsibilities

• Build and maintain shared AI platform infrastructure — compute provisioning, networking, IAM for AI workloads
• Own internal tooling and templates to deploy models/agents consistently
• Standardize CI/CD pipelines for AI workloads across practices
• Manage platform cost governance and capacity planning across engagements
• Own platform security posture with AI Security Engineers
• Partner with MLOps/LLMOps Engineers on platform-workload boundary
• Balance competing infra requests from multiple practice leads
• Document platform capabilities clearly for self-serve
• Forecast and justify platform spend to leadership

Skills

Platform engineering

Internal developer platforms

Cost governance

Security hardening

Stakeholder management

Cloud platform awareness

Documentation & self-service

MLOps collaboration

Tools

Azure AI Foundry

AWS Bedrock

Google Vertex AI

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
• 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