Staff Data/AI Engineer

RBC•Toronto, ON

4d•Onsite

About The Position

We’re looking for a Staff Data/AI Engineer to join RBC Borealis to design and implement AI solutions across Personal Banking & Commercial Banking. In this role, you will build and scale advanced data and ML engineering pipelines, agentic AI systems, and Large Language Model (LLM) applications. You will ensure solutions are secure and grounded in high-quality, trusted data. Working closely with business and platform teams, you will translate complex banking problems into deployable AI systems that are reliable, explainable, and compliant. This is a unique opportunity to work with a team of passionate builders committed to bringing advanced AI into the enterprise at scale.

Requirements

Bachelor’s degree in Computer Science, Software Engineering, or a related field, with 5+ years of experience in data, software, ML, or AI engineering
Proficiency in writing clean, maintainable code (i.e. Python, Java, SQL, Spark) with strong software engineering fundamentals, including version control, testing, and code review best practices
Experience developing and integrating AI-powered systems such as LLM applications, RAG pipelines, or agent-based workflows into secure, production environments
Deep understanding of data modeling, distributed systems, storage optimization, and performance turning for large-scale AI and analytics workloads
A solid understanding of the ML data lifecycle, including feature engineering, model integration, deployment support, and monitoring
Excellent collaboration and communication skills, with the ability to translate ambiguous business problems into well-structured technical designs, collaborating effectively with product, platform, and cross-functional engineering teams

Responsibilities

Build and maintain scalable batch and streaming data pipelines to ingest, transform, and curate high-quality datasets for AI and agentic workflows
Implement DataOps and MLOps automation, including CI/CD pipelines, data validation checks, ML model deployment, monitoring, and drift detection
Develop and integrate AI services such as LLM-powered APIs, retrieval-augmented generation (RAG) pipelines, and agent orchestration logic into secure, production-grade environments
Design and optimize data models, feature stores, and storage patterns to ensure performance, reliability, and governance across AI workloads
Write production-quality code, conduct code reviews, troubleshoot performance issues, and continuously improve system reliability, scalability, and observability