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

Systems Limited

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