Data/AI Engineer

Royal Bank of Canada•Toronto, ON

8h•Onsite

About The Position

Join our team as a Data/AI Engineer and play a key role in building the data foundation for AI-driven innovation. You'll transform raw, unstructured data into clean, reliable datasets that power machine learning applications. Working alongside ML Developers and Researchers, you'll design and implement data pipelines, build monitoring services, and help turn complex data challenges into competitive advantages. If you're passionate about data and excited by the potential of AI, this is your opportunity to make a real impact.

Requirements

Bachelor’s degree in computer science, Software Engineering, or related field, plus experience in data, software, ML, or AI engineering
Strong SQL and database fundamentals (both relational and NoSQL)
Proficiency in production-grade coding with strong software engineering fundamentals including version control, testing, and code review practices
Deep expertise in data modeling, distributed systems, storage optimization, and performance tuning for large-scale AI and analytics workloads
Solid understanding of the ML data lifecycle, including feature engineering, model integration, deployment support, and monitoring
Excellent communication and collaboration skills with the ability to translate complex business requirements into well-architected technical solutions; proven ability to work effectively across product, platform, and cross-functional engineering teams

Responsibilities

Design and optimize data models, feature stores, and storage patterns to ensure performance, reliability, and governance across AI workloads
Build and maintain scalable batch and streaming data pipelines that ingest, transform, and curate high-quality datasets for AI and agentic workflows
Implement DataOps and MLOps automation, including CI/CD pipelines, data validation, ML model deployment, monitoring, and drift detection
Write production-quality code, conduct thorough code reviews, troubleshoot performance issues, and continuously enhance system reliability, scalability, and observability