AI Platform Engineer

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