AI Platform Architect

We are looking for a Lead AI Platform Architect with strong hands-on experience designing and building enterprise-scale AI/ML platforms and cloud-native infrastructure. The ideal candidate must be able to discuss their hands-on project experience in depth, particularly around Kubernetes/OpenShift, AI/ML platforms, MLOps/AI Ops, and cloud architecture. This role will lead technical architecture, define long-term platform strategy, influence technology roadmaps, and drive cross-functional initiatives. Key Responsibilities Lead architecture and technical direction for:Enterprise GenAI Platforms Agentic AI Platforms Model Serving Infrastructure Agent Runtime Platforms Prompt & Evaluation Frameworks AI Governance & Guardrails AI Observability & AI Operations Design and drive multi-cloud AI platform architecture across:OpenShift AI / RHOAI Google Cloud Platform Vertex AI Azure AI Foundry AWS Bedrock Gemini, Anthropic Claude, and OpenAI models Define platform strategies for:Kubernetes/OpenShift GPU infrastructure NVIDIA SuperPOD Capacity planning Multi-region architectures High availability Disaster recovery Active-active architectures Drive architecture for highly scalable, secure, resilient Tier-1 production platforms. Partner with engineering, infrastructure, data, security, and business teams to establish technical roadmaps and architectural standards. Must-Have Skills Candidates should have strong hands-on experience with: Kubernetes / OpenShift AI/ML Platforms MLOps / AI Ops Cloud Architecture Distributed Systems Generative AI / LLM Platforms RAG Architectures Agentic AI Model Serving API Platforms Data Platforms High Availability & Disaster Recovery Production-scale cloud-native platforms Preferred Experience 10+ years of experience in software engineering, infrastructure, and/or architecture. 5+ years designing and implementing large-scale cloud-native platforms. Experience with OpenShift AI / RHOAI, Google Cloud Platform Vertex AI, Azure AI Foundry, AWS Bedrock, or similar AI platforms. Experience with GPU infrastructure and NVIDIA SuperPOD environments. Experience architecting multi-cloud and multi-region platforms. Experience with enterprise AI governance, security, observability, and guardrails. What the Client Is Looking For Hands-on technical depth is critical. Candidates should be able to clearly explain: AI/ML platforms they have personally designed or implemented. Kubernetes/OpenShift architecture and production deployments. MLOps/AI Ops implementation. Cloud architecture decisions and trade-offs. GenAI, RAG, agentic AI, or model-serving projects. Scalability, resiliency, HA, and DR strategies used in real production environments. Location Preference: Bay Area preferred; Charlotte considered as a secondary location.