Data​/AI Engineer

Staff Data/AI Engineer

We're looking for a Staff Data/AI Engineer to join RBC Borealis to design and implement AI solutions across Personal Banking and 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 passionate team committed to bringing advanced AI into the enterprise at scale.

• Build and maintain scalable batch and streaming data pipelines to ingest, transform, and curate high-quality datasets for AI and agentic workflows.
• Implement data‑ops 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.

• Master’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 (Python, Java, SQL, Spark) with robust 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 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 collaboration and communication skills, with the ability to translate ambiguous business problems into well‑structured technical designs and collaborate effectively with product, platform, and cross‑functional engineering teams.

• Comprehensive Total Rewards Program including bonuses and flexible benefits, competitive compensation, commissions, and stock options where applicable.
• Leadership support for development through coaching and management opportunities.
• Opportunity to make a lasting impact from a local‑to‑global scale.