Principal AI Platform Architect LLMOps
Job Description & How to Apply Below
This Principal AI Engineer will establish the platform architecture, runtime, standards, and pipelines supporting AI-assisted delivery. The mandate includes model lifecycle management, cost and latency optimization, context management, observability, reliability, and auditable code promotion. You will partner with Risk, Engineering, senior leadership, and cohort teams.
Key Responsibilities:
Define platform standards, runtime architecture, and technical roadmap
Establish LLMOps, evaluation, governance, RAG, and optimization practices
Build monitoring distinguishing model, code, and platform failures
Engineer rollback, circuit breakers, degradation, and zero-downtime deployment
Present architecture, progress, impact, and direction to executives
Requirements:
Deep prompt engineering and model lifecycle management experience
Expertise in RAG, context management, and evaluation frameworks
Mastery of CI/CD, Docker, Kubernetes, and infrastructure-as-code
Experience with AWS, Azure, GCP, or enterprise platforms
Background building scalable automated testing and validation
Apply LLM engineering, platform architecture, evaluation, and reliability expertise to RBC’s governed AI-DLC delivery foundation.
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