AI Platform Architect

• 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.

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., 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.