Senior AI Engineer

RBC•Toronto, ON

2d•Onsite

About The Position

As a Senior AI Engineer on the ASEDA team, you'll be 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 build MCP servers, agent orchestration pipelines, and RAG systems that connect large language models to the enterprise. You'll own complex features end-to-end, collaborate closely with Staff and Principal Engineers on architecture, and raise the quality bar for the team. This is a role where your code ships, your technical depth matters, and you'll grow rapidly in one of the most exciting areas in software. We value positive attitude, willingness to learn, open communication, teamwork, and commitment to clean, secure and well-tested code.

Requirements

4-6 years building production software systems — you know what it takes to ship and operate reliable code at scale
1–2 years working hands-on with LLMs and generative AI in production or serious prototyping
Working knowledge of 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 or contributing to RAG systems with vector databases, embeddings, and retrieval strategies
Familiarity with MCP or similar agent-tool integration frameworks
Comfort with cloud platforms (AWS, Azure, or GCP), containers (Docker/Kubernetes), and CI/CD
Strong collaboration skills — you thrive in code reviews, design discussions, and pair programming
Clear communicator who can explain technical decisions to both peers and business stakeholders
Understanding of security, data governance, and responsible AI practices

Nice To Haves

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 — versioning, evaluation, and reproducibility

Responsibilities

Build agentic AI systems — design and implement agent orchestration, MCP servers, RAG pipelines, and multi-agent workflows that run reliably in production
Own complex features end-to-end — take ambiguous requirements and deliver well-architected, well-tested solutions from design through deployment
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 — contribute to evaluation frameworks, prompt versioning, guardrails, and observability so agent behavior stays reliable across model updates
Collaborate and learn — work closely with Staff and Principal Engineers on architecture decisions, participate in code reviews, and share knowledge with the team
Stay at the frontier — explore emerging models, frameworks, and protocols (MCP, A2A) and bring the best ideas back to the team