Principal Agentic AI Platform Engineer
2 hours ago
Winnipeg, Manitoba, Canada
rbc
Full-time
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Lead agentic AI architecture for RBC’s AI-DLC Platform, defining governed automation, production boundaries, and enterprise-scale engineering standards.
As Principal AI Engineer, you will own the platform’s technical direction and 12–18 month roadmap. You will guide hybrid skills, agents, and software-factory evolution. Your work spans LangGraph, CrewAI, AutoGen, RAG, MCP services, APIs, LLMOps, governance, and production reliability.
Key Responsibilities:
Own architecture across skills, commands, agents, context, MCP, and governance Define autonomy models, controls, approvals, and evidence requirements Design multi-agent workflows with planning, memory, tools, and guardrails Operate auditable promotion pipelines through Harness and Helios Track adoption, quality, security, cycle time, usage, and ROI Requirements:
Proven production expertise with agentic AI architectures Hands-on LangGraph, CrewAI, or AutoGen proficiency Deep LLM engineering, RAG, evaluation, and LLMOps competency Strong FastAPI, gRPC, MCP, CI/CD, Docker, and Kubernetes capability Experience with cloud providers and enterprise platforms Shape RBC’s AI-DLC Platform through agentic architecture, LLMOps, governed delivery, and measurable enterprise engineering outcomes.
Own architecture across skills, commands, agents, context, MCP, and governance Define autonomy models, controls, approvals, and evidence requirements Design multi-agent workflows with planning, memory, tools, and guardrails Operate auditable promotion pipelines through Harness and Helios Track adoption, quality, security, cycle time, usage, and ROI Requirements:
Proven production expertise with agentic AI architectures Hands-on LangGraph, CrewAI, or AutoGen proficiency Deep LLM engineering, RAG, evaluation, and LLMOps competency Strong FastAPI, gRPC, MCP, CI/CD, Docker, and Kubernetes capability Experience with cloud providers and enterprise platforms Shape RBC’s AI-DLC Platform through agentic architecture, LLMOps, governed delivery, and measurable enterprise engineering outcomes.