Principal AI Engineer

Job Description

Job Description What's the opportunity? We are looking for a Principle AI Engineer to drive the development of Data engineering solutions on RBC’s Enterprise Data and AI Hybrid Multi-cloud Platforms, that meet the strategic data objectives of the business. This is unique opportunity to be an impactful Data Engineering leader on a fast growing team. The successful candidate will be responsible for leading the design, development, and implementation of data solutions, as well as lead, mentor, and grow a team of talented data engineers. This role requires strong data engineering skills and leadership, effective written and verbal communication skills, a strong work ethic and a demonstrated capability to multi-task effectively as a member of a dynamic, fast paced team. At RBC Borealis, you’ll be joining a team that works directly with leading researchers in machine learning, has access to rich and massive datasets, and offers the computational resources to support ongoing development in areas such as reinforcement learning, unsupervised learning and computer vision. You can find out more about our research areas at rbcborealis.com. Your responsibilities include: Oversee end-to-end data integration, including sourcing, lineage, transformation, and storage to enable complex AI and advanced analytics, leveraging extensive technical expertise. Collaborate with Business architecture, System architecture, Business SME and Data Stewards. Architect and implement agentic systems, including tool using agents, workflow orchestrators, and multi step reasoning pipelines that reliably execute business tasks. Design and deliver Retrieval Augmented Generation solutions, including document ingestion, chunking, indexing, vector search, hybrid search, reranking, and grounding strategies over curated data products. Build evaluation harnesses and quality gates, including offline test sets, golden datasets, regression suites, and metrics for factuality, safety, latency, cost, and business outcomes. Implement observability for AI systems, including tracing across prompts and tool calls, telemetry, drift detection, and runbooks for production operations Lead the build of batch and real time data pipelines, including inbound, outbound, and event driven flows that power analytics and AI use cases. Design governed data products with clear contracts, documentation, lineage, and SLAs, enabling consistent consumption across domains. Establish high quality ingestion, transformation, and serving patterns using lakehouse and warehouse paradigms, plus streaming where appropriate. Partner with data stewards and domain teams to define data standards, quality controls, and metadata that ensure trust and reusability Design and build backend services and APIs that expose data products, agent capabilities, and AI workflows as reliable, secure services. Apply rigorous engineering practices, including code quality, automated testing, CI/CD, performance engineering, and secure by default desi

Required Skills