Senior Director, AI Architecture

What is the opportunity?
This leader sets one standard way to build, secure and run AI across RBC Technology & Operations. They own the reference architecture for retrieval-augmented generation (RAG), AI agents, model access and AI-native software delivery (AI-DLC). The role turns the 2030 T&O ambitions, including AI-native delivery and 30% of GenAI volume on open-weight models, into patterns teams actually use.

Job Description
Success means fewer bespoke stacks, faster delivery with maintainable code, and AI that Internal Audit, Model Risk and OSFI can trust.

What will you do

AI architecture standards as code. Define the enterprise AI reference architecture and approved patterns. Encode them as harness skills, agent constitutions and automated fitness checks, so standards apply at generation time rather than at review.

Common RAG substrate. Converge the many RAG solutions onto one shared platform: ingestion, chunking, embeddings, hybrid retrieval, reranking and evaluation. Retrieval must respect source entitlements by default. Set a retirement plan for duplicate stacks.

Agent sandboxing and security architecture. Design the sandbox standard for every agentic workload: ephemer

AI-DLC harness and quality gates. Own the harness architecture from spec to release. Make quality gates deterministic and mandatory: tests and coverage, static analysis, complexity and duplication limits, dependency and license checks, security scanning and architecture fitness checks. Enforce separation of duties (the agent that writes a change cannot approve it), PR size limits and traceability from spec to deployment.

Skills and evaluation lifecycle. Treat skills like code: owned, versioned, regression-tested with evals and retired when stale. Re-run evals before any model upgrade reaches teams.

Model gateway and inference architecture. Route work across frontier and open-weight models by cost, risk and jurisdiction. Build in token metering, caching and data-classification rules so cost and residency are enforced by the platform.

Risk-tiered control model. Partner with Internal Audit, Model Risk and Cyber to define human-oversight tiers, from human-on-the-loop for low-risk work to human-in-the-loop for payments and core banking. Align with OSFI Guidelines E-23 and B-13.

Design authority and innovation. Chair AI architecture review, keep decision records and manage exceptions. Graduate proven lab work into the platform and evaluate emerging technology, such as agent protocols and new open-weight models.

What do you need to succeed?

15+ years in architecture or engineering, including 5+ years leading architects or engineers at enterprise scale.

Hands-on depth in LLM systems: RAG, agent frameworks, MCP, evaluation and inference serving on cloud and on-premises GPUs.

Security architecture for AI, including the OWASP Top 10 for LLM applications, sandboxing and identity for non-human actors.

Strong Dev Sec Ops  and SDLC background; you have shipped quality gates engineers accepted rather than routed around.

Experience in a regulated industry, ideally financial services under OSFI.

Credibility with engineers: you still prototype and read code. Able to explain trade-offs plainly to executives and audit.

What’s in it for you?
We thrive on the challenge to be our best, progressive thinking to keep growing, and working together to deliver trusted advice to help our clients thrive and communities prosper. We care about each other, reaching our potential, making a difference to our communities, and achieving success that is mutual.

A comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock where applicable

Leaders who support your development through coaching and managing…