AI Platform Architect

Job Description

Job Description

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.