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Job Description

What is the opportunity?

We're looking for an experienced Senior Principal AI Engineer who will bring strategic leadership and deep subject-matter expertise in designing and implementing enterprise-scale AI/ML infrastructure, automation tools (MLOps and DevOps), and cloud-native AI solutions. This is a unique opportunity to lead and shape the future of machine learning infrastructure and work with a team of passionate individuals committed to bringing ML to the enterprise.

What will you do?

• Leading the design, architecture, and optimization of machine learning deployment tools and automation systems that operate the business's data and ML applications
• Defining and implementing best practices and standards for data and machine learning pipelines across the organization
• Collaborating with engineers and machine learning researchers to automate code analysis, build, integration, and deployment of ML applications
• Providing technical guidance on infrastructure design decisions and monitoring solutions
• Building highly scalable, resilient cloud and on-premise systems for hosting machine learning systems using state-of-the-art technologies, including Google Cloud Platform (GCP) and Vertex AI
• Mentoring junior engineers and contributing to team growth and technical excellence

What do you need to succeed?

Must have

• Extensive experience designing and implementing distributed ML systems at scale
• Hands-on experience with GCP services, particularly Vertex AI for model training, deployment, and monitoring
• Experience building and integrating Generative AI solutions, including LLMs, RAG architectures, and AI harnesses/orchestration frameworks (e.g., LangChain, LlamaIndex)
• Proficiency with MLOps orchestration tools such as AirFlow, KubeFlow, Dagster, or Flyte
• Experience with DevOps pipelines (Jenkins, GitHub Actions)
• Strong programming skills in Python; familiarity with Bash or JavaScript a plus
• Hands-on experience deploying hybrid environments across GCP, AWS, and/or Azure
• Familiarity with ML frameworks such as PyTorch or TensorFlow
• Knowledge of software engineering best practices including testing, code reviews, and source control

About the AI Group

RBC's AI Group is the AI accelerator for RBC, with a focus on driving the shift from early-stage AI projects to scaled, client outcomes that amplify the impact of RBC's people. In addition to helping scale the biggest AI opportunities at RBC, the AI Group is responsible for advancing research into emerging use cases across generative and agentic AI, while maintaining expertise in security, responsible AI and regulatory expectations. The Business Enablement function within the AI Group partners with LOBs and Functions to set AI ambition, originate transformation opportunities, and frame programs for delivery — ensuring RBC remains at the frontier of AI-enabled value creation.