Staff Data & AI Engineer - Scalable AI & LLM Systems

RBC; Toronto; On-site; CAD 140,000 - 190,000; Full time

Benefits offered by this job

Bonuses

Flexible benefits

Stock options

Coaching and development

Job summary

RBC Borealis is seeking a Staff Data/AI Engineer to design and implement AI solutions across Personal and Commercial Banking. You will build scalable data and ML pipelines, agentic AI systems, and LLM applications, grounded in high-quality data and secure practices.

You will collaborate with business and platform teams to translate banking problems into reliable, explainable, compliant AI deployments that scale enterprise-wide.

Qualifications

• Bachelor’s degree in Computer Science, Software Engineering, or a related field.
• 5+ years of experience in data, software, ML, or AI engineering.
• Proficiency in Python, Java, SQL, Spark with strong engineering fundamentals.
• Experience deploying AI/ML systems in production.
• Ability to translate complex problems into deployable designs.

Responsibilities

• Build and maintain scalable batch and streaming data pipelines for AI workflows.
• Implement DataOps and MLOps automation including CI/CD and monitoring.
• Develop AI services such as LLM-powered APIs and RAG pipelines.
• Design data models, feature stores, and storage patterns for reliability and governance.
• Write production-grade code, review code, and improve system performance.

Skills

Collaborative teamwork

Communication

Problem solving

Adaptability

Education

Bachelor’s degree in Computer Science or related field

Tools

Python

Java

SQL

Spark

Job description

RBC Borealis is seeking a Staff Data/AI Engineer to design and implement AI solutions across Personal and Commercial Banking. You will build scalable data and ML pipelines, agentic AI systems, and LLM applications, grounded in high-quality data and secure practices.

You will collaborate with business and platform teams to translate banking problems into reliable, explainable, compliant AI deployments that scale enterprise-wide.