Requirements
The ideal candidate will have 6-12+ years of platform/infrastructure engineering experience, with at least 2 years supporting AI/ML workloads. Must possess deep cloud infrastructure expertise (IaC, Kubernetes, networking, IAM), experience building internal developer platforms/tooling, and familiarity with multi-tenant capacity planning, cost allocation, and platform security hardening. Experience integrating managed AI/agentic platforms is a plus.
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 workloadsOwn the internal tooling and templates practices use to deploy models/agents consistentlyStandardize CI/CD pipelines for AI workloads across practices, including shared AI evaluation platformsManage platform-level cost governance and capacity planning across concurrent engagementsOwn platform security posture in partnership with AI Security EngineersPartner with MLOps/LLMOps Engineers on the boundary between platform and workload-specific operationsBalance competing infrastructure requests from multiple practice leadsDocument platform capabilities clearly enough that practices can self-serveForecast and justify platform spend to non-technical leadershipRequirements:6–12+ yrs platform/infrastructure engineering, with 2+ yrs supporting AI/ML workloads specificallyDeep cloud infrastructure expertise (IaC, Kubernetes, networking, IAM), including hosting vector/graph databasesExperience building internal developer platforms/tooling, not just running infrastructureExperience 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-haveFamiliarity with multi-tenant capacity planning and cost allocationExperience with platform-level security hardeningCross-practice stakeholder management — balances competing infra requests from multiple practice leadsCost/capacity planning literacy — can forecast and justify platform spend to non-technical leadershipDocuments platform capabilities clearly enough that practices can self-serveCollaborative — builds shared infrastructure without becoming a bottleneckSuccess metrics: platform uptime/reliability · cost per workload vs. budget · practice self-service adoption rate