AI Platform Engineer

Systems Limited; Karachi Division; On-site; PKR 2,000,000 - 3,000,000; Full time

Job summary

Systems Limited in Pakistan is seeking an AI Platform Engineer with 6–12+ years of experience to build and operate the shared AI platform infrastructure that underpins AI workloads across our engagements.

You will own internal tooling, standardize CI/CD for AI workloads, manage cost governance, and collaborate with MLOps and AI security teams to ensure scalable, secure, and self-serve platform capabilities.

Qualifications

• 6–12+ years of platform/infrastructure engineering experience.
• 2+ years supporting AI/ML workloads specifically.
• Experience building internal developer platforms/tools.
• Experience with managed AI/agentic platforms (Azure AI Foundry, AWS Bedrock, Vertex AI) and open-source stacks.
• Familiarity with multi-tenant capacity planning and cost allocation.
• Platform security hardening experience.
• Cross-practice stakeholder management and balancing infra requests.
• Ability to document and enable self-service of platform capabilities.

Responsibilities

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

Skills

Platform engineering

Infrastructure provisioning

Cost governance

Security hardening

MLOps collaboration

CI/CD for AI

Stakeholder management

Tools

Azure AI Foundry

AWS Bedrock

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
• 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