Job Description

WHAT IS THE OPPORTUNITY? Join RBC’s Site Reliability Engineering team as a founding member building the bank’s

first-ever Agentic AI platform for Software reliability

and

resiliency . You’ll pioneer intelligent automation systems that autonomously prevent incidents, accelerate response times, and transform how we maintain resilience across enterprise systems. This is a rare opportunity to shape the future of AI-driven reliability at scale. Your innovations will protect millions of daily customer transactions and sign-ins. With a clear technical leadership trajectory, you’ll architect cutting-edge solutions at the intersection of AI and infrastructure, setting the standard for autonomous operations in financial services.

WHAT WILL YOU DO?

Design and implement end-to-end Agentic AI solutions

that autonomously detect anomalies, identify root causes, and resolve incidents with minimal human intervention

Develop intelligent automation frameworks

using

LangChain

and

LangGraph

to create context-aware agents that learn from incident patterns and continuously improve response strategies

Build ML-powered monitoring and alerting systems that distinguish signal from noise, dramatically reducing false positives and improving MTTD (Mean Time to Detect) and MTTI (Mean Time to Identify)

Architect scalable, production-grade solutions on OpenShift and Kubernetes that process real-time system metrics and telemetry data at enterprise scale

Implement infrastructure-as-code using Ansible and containerization (Docker) to ensure reproducibility, consistency, and rapid deployment across environments

Partner with incident management and operations teams to translate operational pain points into AI-driven automation opportunities that measurably reduce toil

Establish and track KPIs focused on reducing MTTR (Mean Time to Resolve), MTTD, and MTTI while improving system reliability





Lead technical design discussions and contribute to architectural decisions that shape RBC’s AI-powered reliability strategy

WHAT DO YOU NEED TO SUCCEED? Must have:

Strong 5 years hands on ML engineering experience with designing, training, and deploying machine learning models in production environments

Proven expertise in

Agentic AI frameworks and tools

(LangChain, LangGraph, AutoGen, CrewAI, or similar) and building autonomous, multi-agent systems

Deep understanding of

Model Context Protocol (MCP)

for enabling AI agents to interact with external systems and data sources

Experience building AI agents with tool-calling capabilities, memory management, and reasoning chains

Proficiency in Python and experience with ML libraries (scikit-learn, TensorFlow, PyTorch, or similar)

Working knowledge of containerization (Docker), orchestration (Kubernetes/OpenShift), and infrastructure-as-code principles (Ansible, Terraform)

Demonstrated ability to translate complex technical concepts into business value and collaborate effectively with cross-functional teams

Nice-to-have:

Prior experience in Site Reliability Engineering, DevOps, or infrastructure monitoring roles

Familiarity with observability tools (Prometheus, Grafana, ELK stack) and incident management platforms (PagerDuty, ServiceNow)

Experience with LLMs, prompt engineering, and retrieval-augmented generation (RAG) architectures

Background in financial services or other highly regulated industries with strict reliability requirements

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 opportunities

Ability to make a difference and lasting impact

Work in a dynamic, cooperative, progressive, and high-performing team

A world-class training program in financial services

Flexible work/life balance options

Opportunities to do challenging work

#LI-POST

#TECHPJ

Job Skills Docker Kubernetes Architecture, LangChain (FrameWork), LangGraph, Machine Learning (ML), Python (Programming Language), Red Hat Ansible, Red Hat OpenShift

Additional Job Details Address RBC WATERPARK PLACE, 88 QUEENS QUAY 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 2026-04-27

Application Deadline 2026-08-21

Note 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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📌 Lead AI/ML Engineer (Toronto)
🏢 RBC
📍 Toronto