Staff Software Engineer - Autonomous Agents

RBC; Toronto; On-site; CAD 80,000 - 100,000; Full time

Job summary

A leading financial institution in Toronto is seeking a Generative AI Data Scientist. This role focuses on building autonomous AI agents and requires a deep understanding of machine learning and diverse programming environments. The ideal candidate will hold a PhD or Master's degree and have significant experience in AI. You'll be working on groundbreaking AI projects that enhance operations and deliver innovative solutions. Join a forward-thinking team that values creativity in a competitive environment.

Benefits

Competitive compensation

Flexible benefits

Mentorship programs

Continuous learning opportunities

Performance bonuses

Qualifications

• 5+ years of research experience focusing on machine learning applications.
• Hands-on experience working with large language models.
• Strong understanding of ML techniques: supervised/unsupervised learning and deep neural networks.

Responsibilities

• Design and develop AI agents for complex ML workflows.
• Architect multi-agent systems with agentic search trees.
• Implement advanced vector search techniques for agent capabilities.

Skills

Machine learning

Prompt engineering

Python

Data analysis

Distributed systems

AI agent design

Communication

Education

PhD or Master's degree in Physics, Computer Science, Data Science, or related field

Tools

NumPy

Pandas

TensorFlow

Keras

PyTorch

FastAPI

Celery

Redis

Kubernetes

Docker

Job description

Overview

Job Description

Intelligent Operations (iO) is an AI-powered safety net for RBC technology operations that keeps services running and reduces outages. We are the first to collect tech operations data from different domains—bringing enormous value from mining information and insights that allow us to predict problems before they happen. This is foundational work for the next generation of AI capabilities at RBC.

We're building the infrastructure that enables AI agents to act autonomously—analyzing data, generating code, executing experiments, and reasoning about operational data. The iO team designs and operates the execution environments, state management systems, and agent orchestration that make autonomous AI safe and reliable. You'll work at the intersection of distributed systems, ML research, and product—building systems that don't exist anywhere else in the industry.

Join RBC's T&O IntOps team as a Generative AI Data Scientist / Researcher and help build AI agents that will lead us to a future where RBC employees can leverage autonomous systems to dramatically increase the scope and quality of what we can accomplish. The ideal candidate will have a passion for developer tools, extensive knowledge of diverse programming environments, and experience with advanced LLM features like tool-use, chaining and orchestration patterns, and prompt engineering.

What will you do?

• Build Autonomous Research Agents: Design and develop AI agents that autonomously execute complex ML workflows—from exploratory data analysis and statistical modeling to visualization and report generation. Agents generate complete Python codebases with modular structure rather than isolated scripts.
• Agent Architecture & Orchestration: Architect multi-agent systems (Developer/Supervisor patterns) with agentic search trees where each node represents a complete solution attempt. Build state management for long-running tasks—handling checkpoints, recovery, and resumption across failures.
• Sandboxed Execution Environments: Design and build secure compute environments where agents can safely execute code, access tools, and interact with external services with process isolation and resource limits.
• Vector Search & Retrieval: Implement advanced vector search techniques and RAG (Retrieval-Augmented Generation) systems to enhance agent capabilities across diverse data sources.
• Evaluation & Benchmarking: Develop evaluation frameworks to measure agent performance, reliability, and task completion. Build observability and debugging tools for agent execution—understanding what the agent did, why, and how to improve it.
• Data Engineering: Build data registry patterns for live database access (PostgreSQL, MySQL, S3) with automated schema discovery and LLM-enhanced metadata.
• Deployment & Integration: Deploy AI agents into production OpenShift/Kubernetes environments with async task processing (Celery/Redis), ensuring scalability and robustness.
• Developer Tools & Experience: Engage directly with developers to gather insights and improve user experience; contribute across the stack—from front-end UI to back-end infrastructure. Dive into Python notebooks, VS Code plug-ins, and containerized environments.
• Collaborative Research: Partner with researchers to improve model capabilities through shared tools and evals. Stay ahead of advancements in AI-assisted coding by experimenting with new tools and evaluating emerging techniques.

What do you need to succeed?

Must-Have:

• PhD or Master's degree in Physics, Computer Science, Data Science, or related field
• 5+ years of research experience with a focus on machine learning applications
• Hands-on experience working with large language models and prompt engineering
• Proficient in Python and libraries: NumPy, Pandas, TensorFlow, Keras, PyTorch, FastAPI
• Strong understanding of ML techniques: supervised/unsupervised learning, deep neural networks, Transformers, and agentic AI patterns
• Experience with async processing frameworks (Celery, Redis) and cloud platforms (Azure, AWS, OpenShift/Kubernetes)
• Familiarity with MongoDB, PostgreSQL, and distributed systems architecture
• Excellent communication skills with ability to lead cross-functional teams
• Certified in both Deep learning specialization and Neural networks.

Nice-to-Have:

• Experience building and maintaining CLI tools and developer tooling
• Background in React development, TypeScript, and modern front-end patterns
• Experience with container orchestration (Docker, Kubernetes, OpenShift) and cloud infrastructure
• Familiarity with LLM observability tools (Langfuse, LangSmith) and tracing
• Experience with code generation systems and automated research workflows
• Background in high-energy physics research (e.g., CERN or similar)
• Published research in reputable journals
• Projects with stringent safety, security and/or compliance requirements

What's in it for you?

• Work on groundbreaking AI projects with significant industry impact
• Be part of a forward-thinking team that values creativity and innovation
• Opportunities for continuous learning through training, workshops, and conferences
• Mentorship programs and career development plans
• Competitive compensation and flexible benefits
• Performance bonuses and recognition programs

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: 2026-01-28

Application Deadline: 2026-02-17

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

Inclusion and Equal Opportunity Employment

At 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.