Staff AI Engineer

Job Description

What is the Opportunity?

Global Functions Technology (GFT) is part of RBC's Technology and Operations division. GFT's impact is far-reaching as we collaborate with partners from across the company to deliver innovative and transformative IT solutions. Our clients represent Risk, Finance, HR, CAO, Audit, Legal, Compliance, Financial Crime, Capital Markets, Personal and Commercial Banking and Wealth Management. We also lead the development of digital tools and platforms to enhance collaboration.

As a Staff Engineer on the ASEDA team, you'll be at the center of RBC's most ambitious AI bet — building the agentic AI and generative AI systems that are transforming how the enterprise operates. Think AI agents that can reason, plan, use tools, and take action across real business workflows — not chatbot demos, but production systems that move the needle.

You'll design and ship MCP servers, agent orchestration pipelines, and RAG systems that connect large language models to the enterprise. You'll work across teams, set technical direction, and build the reusable patterns that let others move faster. This is a role where your code ships, your architecture decisions stick, and your ideas shape how AI gets adopted at scale.

We value positive attitude, willingness to learn, open communication, teamwork, and commitment to clean, secure and well-tested code.

What Will You Do?

• Build agentic AI systems end-to-end — design and implement agent orchestration, MCP servers, RAG pipelines, and multi-agent workflows that run reliably in production
• Set technical direction — make architecture decisions on how agents interact with tools, manage context, route between models, and recover from failures
• Ship production AI — write clean, well-tested Python code for systems that real users and business teams depend on every day
• Drive quality and safety — build evaluation frameworks, prompt versioning, guardrails, and observability so agent behavior stays reliable across model updates
• Enable other teams — create reusable components, run enablement sessions, and mentor engineers on agentic AI patterns and best practices
• Stay at the frontier — evaluate emerging models, frameworks, and protocols (MCP, A2A) and bring the best ideas back to the team

What do you need to succeed?

Must-Have

• 5-8 years building production software systems — you know what it takes to ship and operate reliable code at scale
• 1-3 years working hands-on with LLMs and generative AI in production (not just POCs)
• Solid experience with agentic AI patterns — tool use, agent orchestration, ReAct, multi-step reasoning, memory and context management
• Strong Python skills and a commitment to writing clean, maintainable, production-grade code
• Experience building RAG systems with vector databases, embeddings, and retrieval strategies
• Working knowledge of MCP or similar agent-tool integration frameworks
• Comfort with cloud platforms (AWS, Azure, or GCP), containers (Docker/Kubernetes), and CI/CD
• Proven ability to lead technically — driving cross-team decisions, reviewing architecture, mentoring engineers
• Clear communicator who can explain complex AI trade-offs to both technical and business audiences
• Understanding of security, data governance, and responsible AI practices

Nice-to-Have

• Experience with agent frameworks (LangChain, LangGraph, LlamaIndex, Semantic Kernel, CrewAI)
• Hands-on with AI observability tooling (LangSmith, LangFuse, Weights & Biases)
• Familiarity with multi-agent protocols (A2A) and the broader MCP ecosystem
• Background in NLP, information retrieval, or knowledge graphs
• Experience with prompt/context engineering at scale — versioning, evaluation, and reproducibility

What’s in it for you?

• Work on what matters — agentic AI, MCP, and multi-agent systems are the most exciting frontier in software right now, and you'll be building it for one of the largest enterprises in the country

• Real impact, real visibility — your work will directly power AI capabilities in RBC

• Access to serious resources — leading foundation models, rich enterprise datasets, and significant compute

• Accelerate your growth — work alongside Staff and Principal Engineers, build deep expertise in agentic AI, and grow into technical leadership

• A team that builds — collaborative, fast-moving, and deeply technical — we ship production AI, not slide decks

• Competitive total rewards — compensation, performance bonuses, flexible benefits, and stock options where applicable

Job Skills

Agentic AI, AI Agent Orchestration, AI Systems, Cloud Platform, Collaboration, End-to-End Testing, Generative AI, Mentorship, Model Context Protocol, Production Software, Software Systems, Vector Databases

Additional Job Details

Address:

RBC CENTRE, 155 WELLINGTON ST W:TORONTO

City:

Toronto

Country:

Canada

Work hours/week:

37.5

Employment Type:

Full time

Platform:

TECHNOLOGY AND OPERATIONS

Job Type:

Regular

Pay Type:

Salaried

Posted Date:

2026-08-04

Application Deadline:

2026-08-24

Note: Applications will be accepted until 11:59 PM on the day prior to the application deadline date above

Our Employment Opportunities

At RBC, we are guided by living shared values of Client First, Integrity, Collaboration, Respect and Excellence and winning together as One RBC. We believe an inclusive workplace that has diverse perspectives is core to our continued growth as one of the largest and most successful banks in the world. Maintaining a workplace where our employees feel supported to perform at their best, effectively collaborate, drive innovation, and grow professionally helps to bring our Purpose to life and create value for our clients and communities. RBC strives to deliver this through policies and programs intended to foster a workplace based on respect, belonging and opportunity for all.

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RBC is presently inviting candidates to apply for this existing vacancy. Applying to this posting allows you to express your interest in this current career opportunity at RBC. Qualified applicants may be contacted to review their resume in more detail.