AI Platform Engineer

Systems Limited; Lahore; On-site; PKR 3,000,000 - 5,400,000; Full time

Job summary

Systems Limited is seeking an experienced AI Platform Engineer to design, build, and operate a shared AI platform infrastructure in Lahore. You will own compute provisioning, networking, and IAM for AI workloads while driving internal tooling and templates to standardize deployment of models and agents.

You will collaborate with MLOps/LLMOps and cross-practice leads to align on CI/CD pipelines, cost governance, and security posture.

Qualifications

• 6–12+ yrs platform/infrastructure engineering, with 2+ yrs supporting AI/ML workloads
• Deep cloud infrastructure expertise (IaC, Kubernetes, networking, IAM)
• Experience building internal developer platforms/tooling
• Experience integrating managed AI platforms alongside self-hosted stacks
• Familiarity with multi-tenant capacity planning and cost allocation
• Experience with platform-level security hardening
• Cross-practice stakeholder management
• Cost/capacity forecasting and presenting to leadership
• Documents platform capabilities clearly for self-service

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-level cost governance and capacity planning
• Own platform security posture in partnership with AI Security Engineers
• Partner with MLOps/LLMOps on boundary between platform and workload operations
• Balance competing infrastructure requests from multiple practice leads
• Document platform capabilities for self-service by practices
• Forecast and justify platform spend to non-technical leadership

Skills

Platform engineering

Cloud infrastructure

Kubernetes

IaC (infrastructure as code)

Cost governance

Security hardening

Stakeholder management

Documentation

AI/ML workloads

Developer platforms

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
• 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