The AI-DLC Platform embeds governed, agentic AI across RBC's software development lifecycle - skills, commands and guardrails spanning discovery, build/test/security and release, now running in real repositories. The next step is to take it from a governed foundation (human-approved, A0-A2) to hybrid skills and agents, and ultimately a software factory, with autonomy added only where evidence supports it. As Principal AI Engineer you will own the technical direction of that journey.

You will define the platform architecture, the production boundary between code that runs in production and agents that run in production, and the standards, pipelines and runtime that make AI-assisted delivery auditable, reproducible and secure at enterprise scale. You will provide technical leadership across multiple squads and domains, advise senior leadership on AI-DLC architecture and engineering, and be accountable for the adoption and measurable business impact of the platform. This role suits a technical leader who combines deep agentic-AI and platform engineering skill with the judgment to work with risk, governance and engineering partners and turn policy into something that can be tested.

Own the AI-DLC Platform architecture (skills, commands, agents, context layer, MCP services, governance) and define the 12-18 month technical roadmap from governed foundation through hybrid skills/agents to software factory. Define the autonomy model (A0 and beyond) with evidence, controls and approvals required before capabilities earn more autonomy. Make high-impact build-vs-buy and vendor decisions for AI tooling, models and infrastructure

- Own the promotion pipeline from Harness validation through Helios deployment; ensure AI-generated code is auditable, reproducible, explainable and traceable to specs and prompts.



Define and enforce the boundary between AI-generated code and sanctioned agents in production; codify governance policy with Risk and Engineering teams. Establish coding standards and spec-driven development practices (spec-kit, OpenSpec) so AI output is structurally sound before human review. Design and build multi-agent workflows with orchestration frameworks (LangGraph, CrewAI, AutoGen) including tool use, memory, planning, guardrails and human-in-the-loop patterns. Engineer reliability into production systems through circuit breakers, graceful degradation, rollback and zero-downtime deployment
- Build monitoring and alerting that distinguishes bad models, code and platform issues; use AI to auto-generate test cases and validation checks
- Implement access controls, audit trails and human-in-the-loop validation; own technical risk assessment for AI initiatives. define and track KPIs (cycle time, quality, security, usage) and report to senior leadership. Advise and present to executive steering committees on AI-DLC architecture, direction, progress and impact
- Lead architecture design, code reviews and technical discussions; mentor engineers, participate in hiring and define competency frameworks for AI engineers.

Agentic AI architecture: Proven expertise designing and orchestrating multi-step autonomous agents with tool use, memory, planning, guardrails and human-in-the-loop patterns

- LLM engineering and LLMOps: Deep competency in prompt engineering at scale, RAG and context management, evaluation frameworks, cost/latency optimization, and model lifecycle management in productio.

Tool and data integration: Demonstrated mastery of CI/CD pipelines, container orchestration (Docker/Kubernetes) and infrastructure-as-code

- OpenShift, Kafka, Elasticsearch, Apigee) and cloud providers (AWS, Azure, GCP).

Automated testing and QA: Proficiency using AI to generate tests, analyze logs and run validation checks;

background building scalable test automation.

📌 Principal AI Engineer, AI DLC Platform (Toronto)
🏢 RBC
📍 Toronto