Senior Staff Engineer – Agentic AI

RBC•Toronto, ON

1d•Onsite

About The Position

We're looking for a Senior Staff Engineer with strong hands-on engineering experience and a consulting mindset who will lead workstreams, drive stakeholder engagement, and deliver production-grade agentic AI solutions across RBC. This role bridges deep technical delivery with business advisory capability where you're as comfortable facilitating a discovery session with a business leader as you are writing production code for a multi-agent system. At RBC Borealis, you'll be joining the Agentic AI Enablement team, a forward-deployed engineering force that embeds directly into strategic use cases across Lines of Business. You'll operate as a Tech Lead within squads, independently driving workstreams while partnering with Principal Engineers on broader engagement strategy. You'll work directly with business stakeholders, platform engineers, and LoB technical teams to deliver high-impact solutions and build organizational capability in agentic AI.

Requirements

8-12 years of combined experience in software engineering, solutions architecture, technical consulting, or enterprise AI/ML systems
One or both of the following: Consulting/advisory depth: 3+ years in technical consulting, solutions engineering, professional services, or customer-facing delivery roles where you owned the client relationship and shaped technical approaches to business problems
Agentic AI depth: 2-4 years hands-on experience with agentic AI systems, LLMs, and modern AI/ML frameworks including architecture patterns (ReAct, Tool Use, multi-agent orchestration)
Strong software engineering fundamentals with ability to write clean, maintainable production code (Python preferred)
Demonstrated ability to sit across from a business stakeholder, ask the right questions, and translate ambiguous problems into structured technical approaches
Proficiency in (or ability to rapidly acquire) LLM-based systems including prompt engineering, RAG pipelines, vector databases, and tool integration patterns
Experience designing or implementing Model Context Protocol (MCP) servers or similar integration frameworks
Knowledge of cloud platforms (AWS, Azure, or GCP) and modern DevOps practices including containerization (Docker, Kubernetes) and CI/CD pipelines
Understanding of security best practices, data governance, and compliance requirements in regulated environments
Strong facilitation, communication, and presentation skills. You can run a room, present trade-offs to executives, and write a crisp recommendation
Proven ability to mentor junior engineers and build team capability while maintaining hands-on delivery (60-70% hands-on coding expected)
Fast learner comfortable with ambiguity. You thrive in rapidly evolving technology landscapes and can get productive quickly in new domains
Comfort working in Agile environments with iterative delivery approaches

Nice To Haves

Background in financial services, fintech, or other regulated industries
Experience in technology consulting, solutions engineering, or customer-facing ML/AI delivery roles
Experience leading small teams in delivery settings
Familiarity with enterprise architecture governance processes and frameworks
Experience with agentic AI evaluation frameworks, observability, and production monitoring

Responsibilities

Leading technical workstreams within engagements, including solution design, hands-on build, and delivery of production-grade agentic AI systems
Conducting discovery sessions with LoB stakeholders to understand business challenges, identify automation opportunities, and translate requirements into agentic AI solution specifications
Serving as Tech Lead on Light squad engagements with fractional Principal oversight, owning architecture decisions and delivery outcomes for bounded-scope solutions
Designing and implementing end-to-end agentic solutions including MCP servers, RAG pipelines, agent orchestration, tool integrations, and security/compliance controls
Facilitating technical workshops and presenting solution trade-offs to non-technical stakeholders with clarity and confidence
Mentoring Staff Engineers within the pod, providing code reviews, pairing on complex problems, and supporting their professional growth
Contributing to knowledge transfer and enablement activities, including documentation, patterns, playbooks, and hands-on training for LoB engineering teams
Acting as a bidirectional bridge between LoB teams and Borealis platform engineering, surfacing capability gaps and informing platform roadmap priorities
Supporting production deployments, post-deployment optimization, and contributing reusable components back to shared platforms and repositories
Participating in community of practice activities to share learnings, identify cross-engagement synergies, and shape reference architectures